{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Week 8\n",
    "\n",
    "**`Market-Simulation::evaluation-metrics::moments`**\n",
    "* up to forth order\n",
    "* visualization\n",
    "\n",
    "**`Market-Simulation::evaluation-metrics::arbitrage`**\n",
    "* quadratic programming\n",
    "* random agent\n",
    "* reinforcement agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/omega/Developer/qtrader\n"
     ]
    }
   ],
   "source": [
    "# change current working directory\n",
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# suppress warning messages\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# data provider\n",
    "from qtrader.envs.data_loader import Finance\n",
    "# pandas.DataFrame cleaner\n",
    "from qtrader.utils.pandas import clean\n",
    "# machine floating-point relative accuracy\n",
    "from qtrader.utils.numpy import eps\n",
    "# unique id generator\n",
    "from qtrader.utils import uuid\n",
    "\n",
    "# YAML parser\n",
    "import yaml\n",
    "\n",
    "# scientific programming\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats\n",
    "\n",
    "# visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start date: 2010-01-01\n",
      "trading frequency: W-FRI\n",
      "trading universe: ['AAPL', 'GE', 'JPM', 'MSFT', 'VOD', 'GS', 'TSLA', 'MMM']\n",
      "rolling window size: 20\n"
     ]
    }
   ],
   "source": [
    "# fetch configuration file\n",
    "config = yaml.load(open('config/log/week_8.yaml', 'r'))\n",
    "\n",
    "# configuration summary\n",
    "print(f\"start date: {config['start_date']}\")\n",
    "print(f\"trading frequency: {config['freq']}\")\n",
    "print(f\"trading universe: {config['tickers']}\")\n",
    "print(f\"rolling window size: {config['window']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Data Source\n",
    "\n",
    "Fetch prices, simple-relative returns and log-returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# prices\n",
    "prices = clean(Finance.Prices(config['tickers'],\n",
    "                              config['start_date'],\n",
    "                              freq=config['freq'],\n",
    "                              csv=config['csv_file_prices']))\n",
    "\n",
    "\n",
    "# returns\n",
    "returns = clean(Finance.Returns(config['tickers'],\n",
    "                                config['start_date'],\n",
    "                                freq=config['freq'],\n",
    "                                csv=config['csv_file_returns']))\n",
    "\n",
    "# log-returns\n",
    "rhos = np.log(1 + returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### `Moments`\n",
    "\n",
    "Test statistical moments consistency and deviation between original and generated series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### First Order\n",
    "\n",
    "Test percentage deviations of first order moments of two distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def first_order(df_1, df_2, log=False, render=False):\n",
    "    \"\"\"First order moments comparison test.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    df_1: pandas.DataFrame\n",
    "        Family 1 data.\n",
    "    df_2: pandas.DataFrame\n",
    "        Family 2 data.\n",
    "    log: bool, optional\n",
    "        Flag for printing summary.\n",
    "    render: bool, optional\n",
    "        Flag for printing distributions.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    summary: dict\n",
    "        First order test summary.\n",
    "    \"\"\"\n",
    "    # type check\n",
    "    assert isinstance(df_1, pd.DataFrame)\n",
    "    assert isinstance(df_2, pd.DataFrame)\n",
    "    # interface check\n",
    "    assert (df_1.columns == df_2.columns).all()\n",
    "    # column-wise mean values\n",
    "    mu_1 = df_1.mean(axis=0)\n",
    "    mu_2 = df_2.mean(axis=0)\n",
    "    # relative deviation of mean values\n",
    "    dmu = np.abs((mu_1+eps) / (mu_2+eps) - 1)\n",
    "    # column-wise median values\n",
    "    median_1 = df_1.median(axis=0)\n",
    "    median_2 = df_2.median(axis=0)\n",
    "    dmedian = np.abs((median_1+eps) / (median_2+eps) - 1)\n",
    "    # column-size mode values\n",
    "    mode_1 = df_1.mode(axis=0).iloc[0]\n",
    "    mode_2 = df_2.mode(axis=0).iloc[0]\n",
    "    dmode = np.abs((mode_1+eps) / (mode_2+eps) - 1)\n",
    "    # summary table for each ticker\n",
    "    summary = {}\n",
    "    # iterate over tickers\n",
    "    for ticker in df_1:\n",
    "        # summary table columns\n",
    "        _name1 = 'series 1'\n",
    "        _name2 = 'series 2'\n",
    "        _name3 = 'deviations'\n",
    "        summary[ticker] = pd.DataFrame({_name1: [mu_1[ticker], median_1[ticker], mode_1[ticker]],\n",
    "                                        _name2: [mu_2[ticker], median_2[ticker], mode_1[ticker]],\n",
    "                                        _name3: [dmu[ticker], dmedian[ticker], dmode[ticker]]},\n",
    "                                       columns=[_name1, _name2, _name3],\n",
    "                                       index=['mean', 'median', 'mode'])\n",
    "    # print summary\n",
    "    if log:\n",
    "        for ticker, summ in summary.items():\n",
    "            print(ticker)\n",
    "            print(summ)\n",
    "            print('\\n')\n",
    "    # plot distributions\n",
    "    if render:\n",
    "        # random selection of assets to render\n",
    "        I = np.sort(np.random.choice(df_1.shape[1], min(3, df_1.shape[1]), replace=False))\n",
    "        # initialize figure & axes\n",
    "        fig, axes = plt.subplots(ncols=len(I), nrows=2, sharex=True, figsize=(6.4 * len(I), 4.8))\n",
    "        for i, m in enumerate(I):\n",
    "            # distribution plot of family 1\n",
    "            sns.distplot(df_1.iloc[:, m], label='%s' %\n",
    "                         df_1.columns[m], color='g', norm_hist=True, ax=axes[0, i])\n",
    "            # distribution plot of family 2\n",
    "            sns.distplot(df_2.iloc[:, m], label='%s' %\n",
    "                         df_1.columns[m], color='r', norm_hist=True, ax=axes[1, i])\n",
    "            # y-axis limits\n",
    "            ymin_1, ymax_1 = axes[0, i].get_ylim()\n",
    "            ymin_2, ymax_2 = axes[1, i].get_ylim()\n",
    "            ymin = min(ymin_1, ymin_2)\n",
    "            ymax = max(ymax_1, ymax_2)\n",
    "            # vertical line for mean of family 1\n",
    "            axes[0, i].vlines(mu_1.iloc[m], ymin, ymax, label='%s::mean' %\n",
    "                              df_1.columns[m], color='g', linestyles='-')\n",
    "            # vertical line for median of family 1\n",
    "            axes[0, i].vlines(median_1.iloc[m], ymin, ymax, label='%s::median' %\n",
    "                              df_1.columns[m], color='g', linestyles='-.')\n",
    "            # vertical line for mode of family 1\n",
    "            axes[0, i].vlines(mode_1.iloc[m], ymin, ymax, label='%s::mode' %\n",
    "                              df_1.columns[m], color='g', linestyles='--')\n",
    "            # vertical line for mean of family 2\n",
    "            axes[1, i].vlines(mu_2.iloc[m], ymin, ymax, label='%s::mean' %\n",
    "                              df_2.columns[m], color='r', linestyles='-')\n",
    "            # vertical line for median of family 2\n",
    "            axes[1, i].vlines(median_2.iloc[m], ymin, ymax, label='%s::median' %\n",
    "                              df_2.columns[m], color='r', linestyles='-.')\n",
    "            # vertical line for mode of family 2\n",
    "            axes[1, i].vlines(mode_2.iloc[m], ymin, ymax, label='%s::mode' %\n",
    "                              df_2.columns[m], color='r', linestyles='--')\n",
    "            # setting for family 1\n",
    "            axes[0, i].set(ylabel='Frequency', xlabel='', ylim=[ymin, ymax])\n",
    "            axes[0, i].legend()\n",
    "            # settings for family 2\n",
    "            axes[1, i].set(ylabel='Frequency', xlabel='', ylim=[ymin, ymax])\n",
    "            axes[1, i].legend()\n",
    "        # present figure\n",
    "        fig.show()\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL\n",
      "        series 1  series 2  deviations\n",
      "mean    0.001054  0.000925    0.138724\n",
      "median  0.000897  0.000897    0.000448\n",
      "mode    0.000000  0.000000    0.000000\n",
      "\n",
      "\n",
      "GE\n",
      "        series 1  series 2  deviations\n",
      "mean    0.000374  0.000283    0.321124\n",
      "median  0.000322  0.000322    0.000161\n",
      "mode    0.000000  0.000000    0.000000\n",
      "\n",
      "\n",
      "JPM\n",
      "        series 1  series 2  deviations\n",
      "mean    0.000670  0.000534    0.254623\n",
      "median  0.000521  0.000521    0.000260\n",
      "mode    0.000000  0.000000    0.000000\n",
      "\n",
      "\n",
      "MSFT\n",
      "        series 1  series 2  deviations\n",
      "mean    0.000725  0.000626    0.157918\n",
      "median  0.000323  0.000323    0.000162\n",
      "mode    0.000000  0.000000    0.000000\n",
      "\n",
      "\n",
      "GS\n",
      "        series 1  series 2  deviations\n",
      "mean    0.000367  0.000228    0.613106\n",
      "median  0.000776  0.000776    0.000388\n",
      "mode    0.000000  0.000000    0.000000\n",
      "\n",
      "\n",
      "MMM\n",
      "        series 1  series 2  deviations\n",
      "mean    0.000689  0.000625    0.102478\n",
      "median  0.000737  0.000736    0.000368\n",
      "mode    0.000000  0.000000    0.000000\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1382.4x345.6 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# first order statistics overview\n",
    "summary_first_order = first_order(returns, rhos, log=True, render=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Second Order\n",
    "\n",
    "Test percentage deviations of second order moments. Perform Bartlett, Levene and Fligner-Killeen tests\n",
    "to test variance similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def second_order(df_1, df_2, log=False, render=False):\n",
    "    \"\"\"Second order moments comparison test.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    df_1: pandas.DataFrame\n",
    "        Family 1 data.\n",
    "    df_2: pandas.DataFrame\n",
    "        Family 2 data.\n",
    "    log: bool, optional\n",
    "        Flag for printing summary.\n",
    "    render: bool, optional\n",
    "        Flag for printing distributions.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    summary: dict\n",
    "        Second order test summary.\n",
    "    \"\"\"\n",
    "    # type check\n",
    "    assert isinstance(df_1, pd.DataFrame)\n",
    "    assert isinstance(df_2, pd.DataFrame)\n",
    "    # interface check\n",
    "    assert (df_1.columns == df_2.columns).all()\n",
    "    # covariance matrix of family 1\n",
    "    cov_1 = df_1.cov()\n",
    "    # frobenius norm of family 1\n",
    "    fro_1 = np.linalg.norm(cov_1, ord='fro')\n",
    "    # covariance matrix of family 2\n",
    "    cov_2 = df_2.cov()\n",
    "    # frobenius norm of family 2\n",
    "    fro_2 = np.linalg.norm(cov_2, ord='fro')\n",
    "    # relative deviation of covariances\n",
    "    # normalized by frobenius norms\n",
    "    dcov = np.linalg.norm(cov_1 - cov_2, ord='fro') / \\\n",
    "        (np.sqrt(fro_1) * np.sqrt(fro_2) + eps)\n",
    "    # column-wise std values\n",
    "    std_1 = df_1.std(axis=0)\n",
    "    std_2 = df_2.std(axis=0)\n",
    "    # relative deviation of std values\n",
    "    dstd = np.abs((std_1+eps) / (std_2+eps) - 1)\n",
    "    # summary table for each ticker\n",
    "    summary = {}\n",
    "    # iterate over tickers\n",
    "    for ticker in df_1:\n",
    "        # Bartlett test\n",
    "        bartlett_s, bartlett_p = scipy.stats.bartlett(df_1[ticker], df_2[ticker])\n",
    "        # Levene test\n",
    "        levene_s, levene_p = scipy.stats.levene(df_1[ticker], df_2[ticker])\n",
    "        # Fligner-Killeen test\n",
    "        fligner_s, fligner_p = scipy.stats.fligner(df_1[ticker], df_2[ticker])\n",
    "        # summary table columns\n",
    "        _name1 = 'series 1'\n",
    "        _name2 = 'series 2'\n",
    "        _name3 = 'deviations'\n",
    "        _name4 = 'statistic'\n",
    "        _name5 = 'p-value (>0.05)'\n",
    "        summary[ticker] = pd.DataFrame({_name1: [std_1[ticker], None, None, None],\n",
    "                                        _name2: [std_2[ticker], None, None, None],\n",
    "                                        _name3: [dstd[ticker], None, None, None],\n",
    "                                        _name4: [None, bartlett_s, levene_s, fligner_s],\n",
    "                                        _name5: [None, bartlett_p, levene_p, fligner_p]},\n",
    "                                       columns=[_name1, _name2, _name3, _name4, _name5],\n",
    "                                       index=['std', 'Bartlett', 'Levene', 'Fligner-Killeen'])\n",
    "    # print summary\n",
    "    if log:\n",
    "        for ticker, summ in summary.items():\n",
    "            print(ticker)\n",
    "            print(summ)\n",
    "            print('\\n')\n",
    "    # plot covariance matrices\n",
    "    if render:\n",
    "        # random selection of assets to render\n",
    "        I = np.sort(np.random.choice(\n",
    "            df_1.shape[1], min(3, df_1.shape[1]), replace=False))\n",
    "        # fetch sub-matrices\n",
    "        sub_cov_1 = cov_1.values[np.ix_(I, I)]\n",
    "        sub_cov_2 = cov_2.values[np.ix_(I, I)]\n",
    "        # half-cov mask\n",
    "        mask = np.ones_like(sub_cov_1)\n",
    "        mask[np.triu_indices_from(mask)] = False\n",
    "        # initialize figure & axes\n",
    "        fig, axes = plt.subplots(ncols=3, figsize=(19.2, 4.8))\n",
    "        # family 1\n",
    "        sns.heatmap(sub_cov_1, xticklabels=df_1.columns[I], yticklabels=df_1.columns[I],\n",
    "                    ax=axes[0], mask=mask, cmap=plt.cm.Greys)\n",
    "        axes[0].set(title='Covariance Matrix: Series 1')\n",
    "        # family 2\n",
    "        sns.heatmap(sub_cov_2, xticklabels=df_2.columns[I], yticklabels=df_2.columns[I],\n",
    "                    ax=axes[1], mask=mask, cmap=plt.cm.Greys)\n",
    "        axes[1].set(title='Covariance Matrix: Series 2')\n",
    "        # absolute difference\n",
    "        sns.heatmap(np.abs(sub_cov_1 - sub_cov_2),\n",
    "                    xticklabels=df_1.columns[I], yticklabels=df_1.columns[I],\n",
    "                    ax=axes[2], mask=mask, cmap=plt.cm.Greys)\n",
    "        axes[2].set(title='Covariance Matrix: Absolute Difference')\n",
    "        # present figure\n",
    "        fig.show()\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL\n",
      "                 series 1  series 2  deviations  statistic  p-value (>0.05)\n",
      "std              0.015991  0.016001    0.000625        NaN              NaN\n",
      "Bartlett              NaN       NaN         NaN   0.000777         0.977769\n",
      "Levene                NaN       NaN         NaN   0.000517         0.981869\n",
      "Fligner-Killeen       NaN       NaN         NaN   0.000662         0.979471\n",
      "\n",
      "\n",
      "GE\n",
      "                 series 1  series 2  deviations  statistic  p-value (>0.05)\n",
      "std              0.013491  0.013473    0.001277        NaN              NaN\n",
      "Bartlett              NaN       NaN         NaN   0.003237         0.954626\n",
      "Levene                NaN       NaN         NaN   0.000157         0.990013\n",
      "Fligner-Killeen       NaN       NaN         NaN   0.000735         0.978372\n",
      "\n",
      "\n",
      "JPM\n",
      "                 series 1  series 2  deviations  statistic  p-value (>0.05)\n",
      "std              0.016484   0.01649    0.000319        NaN              NaN\n",
      "Bartlett              NaN       NaN         NaN   0.000203         0.988643\n",
      "Levene                NaN       NaN         NaN   0.000116         0.991425\n",
      "Fligner-Killeen       NaN       NaN         NaN   0.000140         0.990567\n",
      "\n",
      "\n",
      "MSFT\n",
      "                 series 1  series 2  deviations  statistic  p-value (>0.05)\n",
      "std               0.01406  0.014053    0.000461        NaN              NaN\n",
      "Bartlett              NaN       NaN         NaN   0.000421         0.983625\n",
      "Levene                NaN       NaN         NaN   0.000610         0.980294\n",
      "Fligner-Killeen       NaN       NaN         NaN   0.000696         0.978946\n",
      "\n",
      "\n",
      "GS\n",
      "                 series 1  series 2  deviations  statistic  p-value (>0.05)\n",
      "std              0.016668  0.016738     0.00417        NaN              NaN\n",
      "Bartlett              NaN       NaN         NaN   0.034706         0.852214\n",
      "Levene                NaN       NaN         NaN   0.000434         0.983372\n",
      "Fligner-Killeen       NaN       NaN         NaN   0.000021         0.996348\n",
      "\n",
      "\n",
      "MMM\n",
      "                 series 1  series 2  deviations     statistic  p-value (>0.05)\n",
      "std              0.011287  0.011308    0.001841           NaN              NaN\n",
      "Bartlett              NaN       NaN         NaN  6.745846e-03         0.934541\n",
      "Levene                NaN       NaN         NaN  7.792055e-07         0.999296\n",
      "Fligner-Killeen       NaN       NaN         NaN  1.050480e-04         0.991822\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
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AO4D7qK6pc8Kw5esDC6juxLI9cGJm3liW7Q/MAlYDt2Tm6aV9GnA81cVRpwEfzMxHIuJA4I3ALcDrgC9k5hVlmw+VdR8o73NwPndnG0mSxuU1YSRJkjSwImJDqouJTs/MJyJiIXBqZi7tWOfDwDOZeVK5a8upmblHRGxJdaHXWZmZEbGc6qKnN0XE96nuhnJVRBwBbJ6Zx0fER4HPZebj5VboZ2bmjHKnq59SXYT2mYj4LvDNzPzaWt0hkqQJreczYb70pS9Z1emRww47bPyV1LWf/ex53XFOY9hmm21iTbaPiNp5kZlr9J5ac6eddpo53yOHHHJI00NoFXO+9wYk53cFfp6ZT5TXl1Pd3nZpxzpzgI+U7VdGxI4RMRXYD/hRPvet4zJgdkTcRnXr2OUdfZ4JHJ+Zn+rodwrVbYIBHgWeBKZS3bHkxcB1dT/fRPDLX/7SnO+Rl7zkJU0PoXXM+t4akJyfVLwmjCRJkgZGRHxxWNPmwMMdr1eVtm7WGa19U+CxjuLMb/UZEQH8NdXtjcnMVVS3t/1GRJwN3El1KpMkSV2zCCOpMRFR+yFJmjieT85n5uHDurkP2Kjj9dTS1s06o7U/AGwQz/1h+Y0+S/tngbMzc1lpey1VEWZOZh5Y+phfc5dIUqt4PF+fRRhJjTG0JandepTzy4CtI2K98no34LyI2KSccgRwHtVpS5RrwlxTZq4sAXbqKLbsCizOzKeAi4BdOvss278A+DywKDO/HxHvLOtsATyUmU+X1/cA66/pPpKkiczj+fq8O5KkxhjCktRuvcj5zHw0Ig4FTomI+4EVmbk0Ik4CHgJOpCqaLIiI44DtgIPLtndGxALg5IhYTXWR3ZtK14cA8yNiX2ArymlHVDNg3gbMLOPfFlgIfB94S0R8juqaMK8BjlzjDyhJE1idnM9MMpOImJuZi/o4rIFmEUZSY6ZMcTKeJLVZr3I+M88Hzh/WdkzH88eAEe9okJnnAOeM0H4bcNAI7UfxXEGms331aO8hSZPV88n5J598ctIWYMAijKQGORNGktrNnJekdjPn67MII6kxhrYktZs5L0ntZs7XZxFGUmMMbUlqN3NektrNnK+v50WYlStXsnLlSmbMmMGMGTN63b2kFjG0J6YVK1Y8m/MzZ85sejiSBpg5PzEtXryYJUuWsN9++zF79uymhyNpgJnz9fW8CGPxRVK3DO2JaebMmRZfJHXFnJ+YZs+ebfFFUlfM+fo8HUlSY+pcTf3pp5/m6aefnvS3tJOkicS74ElSu5nz9VmEkdSYOpXzddZZh3XWWYdVq1ZZgJGkCcJvSCWp3cz5+izCSGqMoS1J7WbOS1K7mfP1WYSR1BhDW5LarU7OP/XUUzz11FOedipJE4jH8/VZhJHUGENbktqtTs6vu+66rLvuuvziF7+wACNJE4TH8/VZhJHUGENbktrNnJekdquT808++aQzHrEII6lBXk1dktrNnJekdquT8+uvvz7rr78+DzzwwKQtwIBFGEkN8htSSWo3c16S2s2cr88ijKTGGNqS1G7mvCS1mzlfn0UYSY0xtCWp3cx5SWo3c74+izCSGmNoS1K7mfOS1G7mfH0WYSQ1xtCWpHYz5yWp3cz5+izCSGqMd82QpHYz5yWp3cz5+izCSGpMncr5Y489xuOPP05EzM3MSX1bO0maKPyGVJLazZyvzyKMpMbUCe0NN9yQDTfckF//+tcWYCRpgvDgXJLazZyvr+dFmJUrV7Jy5UpmzJjBjBkzet29pBYxtCemFStWPJvzM2fObHo4kgaYOT8xLV68mCVLlrDffvsxe/bspocjaYCZ8/X1vAhj8UVStwztiWnmzJkWXyR1pU7OP/roozz22GOedjoAZs+ebfFFUlc8nq/P05EkNcbQlqR2q5PzL3rRi3jRi17EI488YgFGkiYIi+31WYSR1Bivpi5J7WbOS1K71cn5F7/4xbz4xS/m4YcfnrQFGLAII6lBzoSRpHYz5yWp3cz5+izCSGqMoS1J7WbOS1K7mfP1WYSR1BhDW5LazZyXpHYz5+uzCCOpMYa2JLWbOS9J7WbO1+fV0iRJkiRJktYCZ8JIaox3zZCkdjPnJandzPn6LMJIaozTFyWp3cx5SWo3c74+izCSGmNoS1K7mfOS1G7mfH0WYSQ1ps70xYcffpiHH36YiJibmYv6OCxJUo84TV2S2q1XOR8R+wDvAO4DMjNPGLZ8fWABcBewPXBiZt5Ylu0PzAJWA7dk5umlfRpwPHAzMA34YGY+EhFTgE8DD5f2szLzyrHGEREnA48CjwA7Akdm5r1l2YeAqcBLgR9k5vfG+qwWYSQ1pk7lfOrUqUydOpWHHnrIAowkTRB+QypJ7daLnI+IDYHTgOmZ+URELIyIvTNzacdqRwK3Z+ZJETEDOAvYIyK2BI4GZmVmRsTyiLgwM28qfc7PzKsi4gjgWKqizLuBqZn54YjYBLgyInYA1htjHL/OzOPKeI8FPgocERFvAPbKzLdExAuB6yPiksz81Wif168nJDVmypQptR+SpImjTr4/8sgj3H333UTE3KbHLUnqTo+O53cFfp6ZT5TXlwNzhq0zB1gGkJkrgR0jYiqwH/CjzMyy3jJgdkSsA+wFLB+hz86+HgIeB6aPNY6hAszQx6aaEQPwRx19PQ1cD/zhWPvMmTCSGuM3pJLUbnVyfuONN2bjjTfmwQcfdMajJE0Q3eb8gw8+yIMPPji0zbzMPKNj8eZUpwYNWVXa6GKd0do3BR7rKM509jnaNpuNN46IeAmwL/DOjr6uH2fsv8EijKTGWISRpHYz5yWp3brN+U033ZRNN90UgKuvvvqMYYvvAzbqeD21tHWzzn3AdsPabwYeADaIiCiFmM4+R+srxxpHRGwMfAk4qMyg6Xbsv2HMuf0R8YGI2HVY214RMX+s7SSpG56O1DxzXlI/mfPNM+cl9VOPcn4ZsHVErFde7wacFxGblFOOAM6jOl2Ick2YazJzFbAE2CmeqwbtCizOzKeAi4BdOvscoa9NgPWB60YbR1lvU6oCzDGZeWtEvHOEvtYBdgD+Y6x9Nt5MmG2AcyLiY1RVoa8CVwL7jLbBZZddxuWXX/7s6912243dd999nLeRNBn5DelAqJ3zl156KZdddtmzr3fffXf22GOPfo9T0gRkzg+E2jl/9tln85WvfOXZ1wcccAAHHnhgn4cpaSLqRc5n5qMRcShwSkTcD6zIzKURcRLwEHAi8HlgQUQcRzXz5eCy7Z0RsQA4OSJWA2eWi/ICHALMj4h9ga2Ao0r7N4FZJRe3At6fmauBEcdRtvkBVf3ka+UzPwwszMwrI+KiiPg01d2RPpiZvxzr845XhPlFZj4UEacDJwB3ZubTEfHoaBvsvvvuFl0kdcVvPAdC7ZzfY489LLpI6oo5PxBq5/yBBx5o0UVSV3qV85l5PnD+sLZjOp4/Bhw2yrbnAOeM0H4bcNAI7c9Q3Smpq3GU9teNMfbPjrZsJOMVYbJ0em9E/Lpc7ffZdklaE35DOhDMeUl9Y84PBHNeUt+Y8/WNV4TZLyJeXJ7vUaYDAbwR+HT/hiVpMvAb0oFgzkvqG3N+IJjzkvrGnK9vvCLMk8Cvy/N/62h/qj/DkTSZ9KpyHhH7AO+gXNU8M08Ytnx9YAFwF7A9cGJm3liW7Q/MAlYDt2Tm6aV9GnA81dXVp1Gd3/lIRBxIdeB6C/A64AuZeUXHe20A/BD4QWYe3ZMP2F/mvKS+8RvSgWDOS+obc76+8Yowx2TmcoCI2AoYqqJf1NdRSZoUehHaEbEhcBowPTOfiIiFEbF3x0W0AI4Ebs/Mk8rV1M+i+jZwS+BoYFZmZkQsj4gLy8W8TgPmZ+ZVEXEE1XmjxwNbAEdm5uMR8QbgTGBGx3t9Erh6jT/Y2mPOS+obD84HgjkvqW/M+frGmzv09oj4Znn+RarbLy0G9u7rqCRNCj26pd2uwM8z84ny+nJgzrB15lDdco7MXAnsWG53tx/wo8wcOi9+GTC73F5uL2D58D4z81OZ+fjQRwAeGXqTiHhfWffW57tPGmDOS+obb1E9EMx5SX1jztc33kyYacB7y/PLM/Ot5f7bv3XlYUmqq9vK+T333MOdd97J008/TUR8MTMP71i8OdUt4oasKm10sc5o7ZsCj3UUZ36rz5KFf0251V1EvBrYITM/EhEzu/pgg2Ea5rykPvG004EwDXNeUp84E6a+8YowN3RcQf3rUP3Vi4ib+zssSZNBt5XwLbbYgi222AKAiy+++PBhi+8DNup4PbW0dbPOfcB2w9pvBh4ANoiIKIWY3+izHLx+Fjg7M5eV5rcDj0fEh4HdgXUj4sjM/IeuPmRzzHlJfdOLbzw97XSNmfOS+saZLfWNt8eGApvMvH2kdkl6viKi9mMEy4CtI2K98no34LyI2KSccgTV1Otdy3vOAK7JzFXAEmCneK7jXYHFmfkU1bnyu3T2WbZ/AfB5YFFmfj8i3gnPnqb0t5l5InAZcNUEKMCAOS+pj3qU8552umbMeUl9UyffH3zwQW688UYiYm7T427SeDNhXhoRW2TmXUMN5YJem/R3WJImg15MX8zMRyPiUOCUiLgfWJGZS8stOB8CTqQqmiyIiOOoZr4cXLa9MyIWACdHxGrgzPLtKMAhwPyI2BfYinLaEdUMmLcBM8v4twUWdnymdwJ/QDUT5k8z8+tr/CH7y5yX1Dfd5vzdd9/N7bff7mmn/WHOS+qbOsfzm222GZttthl33333oj4OaeCNV4RZAHwvIm4F7gVeQXVe6R/1eVySJoFeTV/MzPOB84e1HdPx/DHgsFG2PYcRzovPzNuAg0ZoP4rnCjIj9beQjqLMBGDOS+qbbnN+yy23ZMsttwTgggsu8LTT3jLnJfWNpyPVN2YRJjPvjYg9qKZnbgNcDJyXmU+uhbFJajkv5NU8c15SP/Uo55897bSckrQbcGpEbAI8XU4vHTrt9NLO004jYglwREexZVeqC+0+FRFDp51exW+fdnoysDAzL4mId2bmwsz8VMfnWh948QQowJjzkvrK4/n6xpsJQ/lj9+21MBZJk4yV88Fgzkvql17kvKedrjlzXlK/eDxf37hFGEnqFyvnktRuvcp5TzuVpMHk8Xx9FmEkNcbQlqR2M+clqd3M+foswkhqjNMXJandzHlJajdzvr6eF2FWrlzJypUrmTFjBjNmzOh195JaxMr5xLRixYpnc37mzIl0l1ZJa5s5PzEtXryYJUuWsN9++zF79uymhyNpgJnz9fW8CGPxRVK3rJxPTDNnzrT4Iqkr5vzENHv2bIsvkrpiztfn6UiSGmPlXJLazZyXpHYz5+uzCCOpMVbOJandzHlJajdzvj6LMJIaU6dyfs8993DvvfcSEXMzc1EfhyVJ6hG/IZWkdvN4vj6LMJIaUye0X/GKV/CKV7yCW2+9ddIGtiRNNBZhJKndPJ6vzyKMpMZ4cC5J7VYn5++++27uueeeSf8NqSRNJB7P12cRRlJjDG1Jarc6Ob/FFluwxRZbTPpvSCVpIvF4vj6LMJIaY2hLUruZ85LUbuZ8fRZhJDXG0JakdjPnJandzPn6LMJIaoyhLUntZs5LUruZ8/VZhJHUGENbktrNnJekdjPn67MII6kxU6ZMaXoIkqQ+Muclqd3M+foswkhqjJVzSWo3c16S2s2cr88ijKTGGNqS1G7mvCS1mzlfn0UYSY0xtCWp3cx5SWo3c74+izCSGmNoS1K7mfOS1G51cv6OO+7gzjvvJCLmZuaiPg5roFmEkdSYuqF9xx13TPrQlqSJxCKMJLVbnZzfaqut2Gqrrbjxxhsn9bF8zy9lvHLlSs4991xWrlzZ664ltUxEdP3Yaqut2G233bAA07wVK1bwta99jRUrVjQ9FEkDrk7ODz3UvMWLF3PkkUeyePHipociacCZ8/X1fCbMjBkzmDFjRq+7ldRChvDENHPmTGbOnNn0MCRNAHVy/vbbb3fG44CYPXs2s2fPbnoYkiYAj+fr83QkSY0xtCWp3erk/NZbb83WW2/NDTfcYAFGkiYIj+frswgjqTGGtiS1mzkvSe1mztdnEUZSYwxtSWo3c16S2s2cr88ijKTGGNqS1G7mvCS1mzlfn0UrWt57AAAgAElEQVQYSY0xtCWp3cx5SWo3c74+izCSGmNoS1K7mfOS1G7mfH0WYQbY/Pnzmx5CqxxzzDFND0GSfsPHP/7xpofQKkcffXTTQ5Ak9dk666zT9BDUBxGxD/AO4D4gM/OEYcvXBxYAdwHbAydm5o1l2f7ALGA1cEtmnl7apwHHAzcD04APZuYjETEF+DTwcGk/KzOvHGscZZu/BD4BvCkzr+0Y2xuBNwPPAHsBf56Zd4z2WXtehDnssMN63eWkZRFGbWflfGI65JBDmh5Ca1iEUduZ8xPTS17ykqaHIGmC6EXOR8SGwGnA9Mx8IiIWRsTembm0Y7Ujgdsz86SImAGcBewREVsCRwOzMjMjYnlEXJiZN5U+52fmVRFxBHAsVVHm3cDUzPxwRGwCXBkROwDrjTGOHYEfAo8OG/tU4EOZ+c7y+uvAQ2N93ilrtLckaQ1ERO2HJGniMOclqd26zfbrr7+ec889l7PPPpuI+OKwbnYFfp6ZT5TXlwNzhq0zB1gGkJkrgR1LAWQ/4EeZmWW9ZcDsiFiHalbK8hH67OzrIeBxYPpY48jMqzPzJyPsgrcAj0TEURExH3hdZv56rH3m6UiSGuPBtiS1mzkvSe3Wbc5Pnz6d6dOnA/ClL33p8GGLN6c6NWjIqtLWzTqjtW8KPNZRnOnsc7RtNutiHMNtDbwB+Auq06EuiogHMvPi0TawCCOpMXUOzm+99VZuu+02ImJuZi7q47AkST1iEUaS2q1HOX8fsFHH66mlrZt17gO2G9Z+M/AAsEFERCnEdPY5Wl/ZxTiGWwVcnZlPAUTEMmBP4OLRNvB0JEmNqTM9fZtttuFNb3oTFmAkaeLwdCRJarce5fwyYOuIWK+83g04LyI2KaccAZxHdboQ5Zow12TmKmAJsFM81/GuwOJSFLkI2KWzzxH62gRYH7hutHGMswsuorq475CtgRvH2sCZMJIa48G2JLVb3RmPt956qzMeJWkC6cXxfGY+GhGHAqdExP3AisxcGhEnUV3k9kTg88CCiDiOaubLwWXbOyNiAXByRKwGziwX5QU4BJgfEfsCWwFHlfZvArMi4mOl/f2ZuRoYcRzlc74UOAzYGJgXEedm5pWZ+V8R8S9lrE8B9wBfH+vzWoSR1BiLMJLUbnVyfptttmGbbbbh2muvtQAjSRNEr47nM/N84Pxhbcd0PH+Mqggy0rbnAOeM0H4bcNAI7c9Q3Smpq3GU9l8AnyyP4ctOHamv0ViEkdQYizCS1G7mvCS1mzlfX8+vCbNo0SLmzZvHokV+iSFpbF4rYGIy5yV1y5yfmMx5Sd0y5+vr+UyYuXPnMnfu3F53K6mFDOGJyZyX1C1zfmIy5yV1y5yvz9ORJDXG0JakdjPnJandzPn6LMJIaoyhLUntZs5LUruZ8/VZhJHUmClTen5ZKknSADHnJandzPn63GOSGuOFvCSp3cx5SWq3Ovl+yy23sGTJEiJiUl90ypkwkhrTq4PtiNgHeAdwH5CZecKw5esDC4C7gO2BEzPzxrJsf2AWsBq4JTNPL+3TgOOBm4FpwAcz85GIOBB4I3AL8DrgC5l5RTfjkKTJxqKKJLVbnZzffvvt2X777bnmmmsm9a3XLMJIakwvDs4jYkPgNGB6Zj4REQsjYu/MXNqx2pHA7Zl5UkTMAM4C9oiILYGjgVmZmRGxPCIuzMybSp/zM/OqiDgCOJaqKLMFcGRmPh4RbwDOBGZ0OQ5JmlQswkhSu5nz9VmEkdSYHoX2rsDPM/OJ8vpyYA7QWfyYA3wEIDNXRsSOETEV2A/4UWZmWW8ZMDsibgP2ApZ39HkmcHxmfqqj3ynAIzXGIUmTijMeJandLMLUZxFGUmO6De2rr76aq6++GoC/+7u/m5eZZ3Qs3hx4uOP1qtJGF+uM1r4p8FhHcea3+oxq8H8NHFVjHJI0qTjjUZLazSJMfV6YV1Jjur2I1+te9zoOPvhgDj74YIYVYKD6NnKjjtdTS1s364zW/gCwQTz3V+U3+iztnwXOzsxlNcYhSZNKjy7MO9pMw05zqGYzkpkrgfFmPK7Db894nFO2/1RmPl7au5nxKEmTlhdgr88ijKTG9Ci0lwFbR8R65fVuwHkRsUk5AAc4j+rgmfIN6TWZuQpYAuzUUWzZFVicmU8BFwG7dPZZtn8B8HlgUWZ+PyLeOdY41nQfSdJE1m22X3311Zx66qn8/d//PRHxxWHdOONRkgaURZj6PB1JUmN6EcKZ+WhEHAqcEhH3Aysyc2lEnAQ8BJxIVTRZEBHHAdsBB5dt74yIBcDJEbEaOLNMUQc4BJgfEfsCW/HcQfhngbcBM8v4twUWjjaONf6AkjSBdZvzO+20EzvttBMAf/u3f3v4sMVrOuNxu2HtN9Mx47EUYpzxKEnPg0WV+izCSGpMr0I7M88Hzh/WdkzH88eAw0bZ9hzgnBHabwMOGqH9KJ4ryIw7DkmazHqU88/ONCynAu0GnBoRmwBPl5mNQzMeL+2c8RgRS4AjOootu1JdaPepiBia8XgVvz3j8WSqAvslEfHOzFw42jh68QElaaKyCFOfRRhJjTG0JandnPEoSe3m8Xx9FmEkNWbKFC9LJUlt1qucd8ajJA0mj+frswgjqTFWziWp3cx5SWo3c76+npetFi1axLx581i0aFGvu5bUMl5NfWIy5yV1y5yfmMx5Sd2qk+833HAD3/ve94iIuU2Pu0k9nwkzd+5c5s6d1PtUUpc82J6YzHlJ3TLnJyZzXlK36uT8DjvswA477MDy5csndYXX05EkNcaDc0lqN3NektrNnK/PIoykxhjaktRu5rwktZs5X59FGEmNMbQlqd3MeUlqN3O+Pu8nJakxdS7kdf311/Od73xn0l/IS5ImEi/MK0ntZs7X50wYSY2pE8LTp09n+vTp/PCHP5zUF/KSpInEg21Jajdzvj5nwkhqjJVzSWq3ujMeFy5c6IxHSZpAPJ6vz5kwkhozZYp1YElqszo5/5rXvIbXvOY1zniUpAnE4/n6LMJIaoyVcElqN3NektrNnK/PIoykxhjaktRu5rwktZs5X59FGEmNMbQlqd3MeUlqN3O+PoswkhpjaEtSu5nzktRu5nx9XkVHUmO8mroktZs5L0ntViffr7vuOr7xjW9M+rvgORNGUmM82JakdjPnJand6uT8jBkzmDFjBldcccWkvgueRRhJjfHgXJLazZyXpHYz5+uzCCOpMYa2JLWbOS9J7WbO1zfmNWEi4jsRccDaGoykycVrBTTPnJfUT+Z888x5Sf1kztc33oV5b87Mr9Tp8IwzzmDnnXd+9nHGGWeswfAktdmUKVNqP9Rz5rykvjHnB4I5L6lvzPn6xjsdKUdqjIgjM/MfRlo2b9485s2bt8YDk9R+dSrhK1asYMWKFRx66KFzM3NSX8yrx8x5SX3jN54DwZyX1DfmfH3jlaGOjojVwx7PAJ9bG4OT1G51pi3uuOOOvO9978MCTM+Z85L6pk7Or1ixgnPOOWfS37q0D8x5SX3j6Uj1jVeEOQWYOsJjfp/HJWkSMLQHgjkvqW/q5PtrX/ta3v/+91ts7z1zXlLfeDxf33inIz2Zmb8eof1T/RiMpMnFc0IHgjkvqW/M+YFgzkvqm17lfETsA7wDuA/IzDxh2PL1gQXAXcD2wImZeWNZtj8wC1gN3JKZp5f2acDxwM3ANOCDmflIREwBPg08XNrPyswrxxpH2eYvgU8Ab8rMa0v7LsCRwNXAq4CrMvPLY33W8Yowr4yIA+pezEuSumElfCCY85L6xpwfCOa8pL7pRc5HxIbAacD0zHwiIhZGxN6ZubRjtSOB2zPzpIiYAZwF7BERWwJHA7MyMyNieURcmJk3lT7nZ+ZVEXEEcCxVUebdwNTM/HBEbAJcGRE7AOuNMY4dgR8Cjw4b/suBz5f3WAe4LyK+k5kPjPZ5xytbPQY8Mv5uk6T6nL44EMx5SX1jzg8Ec15S3/Qo53cFfp6ZT5TXlwNzhq0zB1gGkJkrgR0jYiqwH/CjzBy6CPkyYHYpiOwFLB+hz86+HgIeB6aPNY7MvDozfzJ84Jn5vcy8qqPpaeCpsfbZeDNhrs/MhRHxBapzRz9ZKkqStMY82B4I5rykvjHnB4I5L6lvus35iy++mEsuuQSAgw46aF5mntGxeHOqU4OGrCptdLHOaO2bAo91FGc6+xxtm826GMdYDgc+nZm/Gmul8YowAZCZR0TEyQa2pF7y4HwgmPOS+sacHwjmvKS+6Tbn99prL/baay8ADjjggDOGLb4P2Kjj9dTS1s069wHbDWu/GXgA2CAiohRiOvscra/sYhwjioj3Ai/KzE+Ot+54pyPlSM8j4qBuBiJJGnjmvCS1mzkvadAtA7aOiPXK692A8yJik3LKEcB5VKcLUa4Jc01mrgKWADvFc9WgXYHFmfkUcBGwS2efI/S1CbA+cN1o4xhv8BHxF8DmmfnJiJgREa8ca/3xZsK8JyJeU56/tlz0BuB/AP803mAkaSzeNWMgmPOS+sacHwjmvKS+6UXOZ+ajEXEocEpE3A+syMylEXES8BBwIvB5YEFEHEc18+Xgsu2dEbEAODkiVgNndsz4OwSYHxH7AlsBR5X2bwKzIuJjpf39mbkaGHEcABHxUuAwYGNgXkScm5lXRsT/Aj4HXB0RbwN+BzgCuHG0zzteEeZGfrNaNMS/qJLWmNPUB4I5L6lvzPmBYM5L6pte5Xxmng+cP6ztmI7nj1EVQUba9hzgnBHabwN+a9ZfZj5DdaekrsZR2n8BfLI8Otu/S1WY6dp4RZhjRjpvNCIuq/MmkjQSD84HgjkvqW/M+YFgzkvqG3O+vvGKMLdExAsz82mAiHgLsC7w3b6PTFLrGdoDwZyX1Dfm/EAw5yX1jTlf33jTEL8H/ANARHwAOJVqOs/JfR6XpElgypQpXT+uvvpqzjrrLCJibtPjbhlzXlLf1Mn5oYd6zpyX1DfmfH3j7YGbMvPw8vxQ4B2Z+VbKre4kaU1ERNePnXfemXnz5pGZi5oed8uY85L6pk7O//jHP+bLX/6yxfbeM+cl9U2dnB96THbjnY70EEBEbAusl5k/Lu0P9nVUkiYFQ3ggmPOS+qZOzu+8887svPPOLF261GJ7b5nzkvrG4/n6xpsJs125pd1HgW91tG892gaLFi1i3rx5LFrk309JY7NyPhDMeUl9Y84PBHNeUt+Y8/WNNxPmy8A/A/dS7qkdET8AfjraBnPnzmXuXGeRShqfITwQzHlJfWPODwRzXlLfmPP1jVeEeWVm7tLZkJn7RsSf9nFMkiYJL8w1EMx5SX1jzg8Ec15S39TJ+eXLl/Of//mfvOtd75o7ma/zOF4R5hMRccgI7S8Hvt6H8UiaRKycDwRzXlLfmPMDwZyX1Dd1cv71r389r3/96zn//PMnbQEGxi/CnAPsAnwJeKC0BbB/PwclaXLw4HwgmPOS+sacHwjmvKS+MefrG7MIk5nHRsRmwF8Bq4FTM/OhiLh2rYxOUqsZ2s0z5yX1kznfPHNeUj+Z8/WNNxOGzLwfOCEiNgeOjYgnM/P4/g9NUtt5rYDBYM5L6hdzfjCY85L6xZyvb9wiDEBErAO8G/hT4JK+jkjSpGHlfHCY85L6wZwfHOa8pH4w5+sbswgTES8E/hL4MHAFsF9mXh8RL8jM1WtjgJLaq1ehHRH7AO8A7gMyM08Ytnx9YAFwF7A9cGJm3liW7Q/MopqifUtmnl7apwHHAzcD04APZuYjHe+3ADgzM7/Y8T4vB/438DDw+8BXM/O7PfmQfWLOS+onD86bZ85L6idzvr7xZsLcDNwLHApcB2REbAUcDhzT57FJarlehHZEbAicBkzPzCciYmFE7J2ZSztWOxK4PTNPiogZwFnAHhGxJXA0MCszMyKWR8SFmXlT6XN+Zl4VEUcAxwLHR8RU4CXAT0YYzpeAAzLz4Yg4E9h4jT9g/5nzkvrGYvtAMOcl9Y1FmPrGK8LcClxMdUX1namupA7wuj6OSdIk0W1oL168mMWLFwMwZ86ceZl5RsfiXYGfZ+YT5fXlwBygswgzB/gIQGaujIgdSzFlP+BHmZllvWXA7Ii4DdgLWN7R55nA8Zm5CvhWRPzRsM/yMmBr4P0RsUFp/nxXH7BZ5rykvrHYPhDMeUl9YxGmvvGKMPMz89LhjRGxU5/GI2kS6fZCXnPmzGHOnDkA7LfffmcMW7w51TeSQ1aVtm7WGa19U+CxjuLMSH0OtzWwI/DvmXlrRHwS+Cjw8XG2a5o5L6lvenTBRovta8acl9Q3Xpi3vjH32EiBXdp/1J/hSJpMIqL2YwT3ARt1vJ5a2rpZZ7T2B4AN4rk3HKnP4VYB92fmreX1ZcCe42zTOHNeUj91m+2LFy/mz//8z3n3u99NRHxxWDeDWGxfQDVb5qPjbNM4c15SP/XoeH5SsWwlqTE9Cu1lwNYRsV55vRtwXkRsUr4FBTiP6ptUyjT1a8o3nUuAnTqKLbsCizPzKeAiqqnbz/Y5zse5Cfh1RAxNTd8auLHO/pCktuk22+fMmcNXvvIVvvWtb5GZhw/rxmK7JA0oizD1dXWLaknqh16EcGY+GhGHAqdExP3AisxcGhEnAQ8BJ1JNF18QEccB2wEHl23vjIgFwMkRsZrqAow3la4PAeZHxL7AVsBRHeP+ADAT+J2IuD8zv5GZT0fEgcBJEXE78Cqq6xBI0qTVo4PtZ4vt5ZSk3YBTI2IT4OlSVB8qtl/aWWyPiCXAERERZdbLrsAXMvOpiBgqtl9FzWJ7Zv4Ki+2SVCvnly1bxpVXXsm+++47NzMX9XFYA80ijKTG9Ooc0sw8Hzh/WNsxHc8fAw4bZdtzgHNGaL8NOGiUbU4BThmh/TKqb0YlSfQm5y22S9LgqpPzu+22G7vtthvnnXfepC3AgEUYSQ1yOqIktVuvct5iuyQNJo/n6/OaMJIkSZIkSWtBz4swixYtYt68eSxaNKlnGEnqghfympjMeUndMucnJnNeUrfM+fp6fjrS3LlzmTt3bq+7ldRChvDEZM5L6pY5PzGZ85K6Zc7X5zVhJDXG0JakdjPnJandzPn6LMJIaoyhLUntZs5LUruZ8/VZhJHUGENbktrNnJekdjPn67MII6kxhrYktZs5L0ntZs7X5y2qJTWmzlXUL7/8ck466SQiwisFStIE4V0zJKndzPn6LMJImhB23313jj32WDLT+2VKUgtddtllfOYzn7HYLklqNU9H0qSx0UYbNT2E1snMNdreSrikXjLne29t5vwee+zBHnvswXe/+12L7ZJGtdVWWzU9hFZZmzl/2WWXcdlll/G9731v7mT+YjXWdKePoOcdSr3g//D3Xmau0U698sora+fFG9/4Rn+QzTPnNZDM+d4z5yctc14Dy6zvLXN+7XMmjKTG+EdUktrNnJekdjPn67MII6kxhrYktZs5L0ntZs7XZxFGUmMMbUlqN3NektrNnK/PIoykxhjaktRu5rwktZs5X59FGEmNMbQlqd3MeUlqN3O+vilND0CSJEmSJGkycCaMpMZYOZekdjPnJandzPn6ej4TZtGiRcybN49Fixb1umtJLRMRtR9qnjkvqVvm/MRkzkvqljlfX2Rmr/vseYdSL/gPvvcyc4126k9+8pPaefHa177WH2TzzHkNJHO+98z5Scuc18Ay63trUHI+IvYB3gHcVw0rTxi2fH1gAXAXsD1wYmbeWJbtD8wCVgO3ZObppX0acDxwMzAN+GBmPhIRU4BPAw+X9rMy88qxxhERmwAnAj8r7/+RzPzviFgXOAO4Dfhd4O7M/MRYn9/TkSQ1ps4f0UsuuYRLLrmEb3/723Mz06/mJGkC8H+WJKndepHzEbEhcBowPTOfiIiFEbF3Zi7tWO1I4PbMPCkiZgBnAXtExJbA0cCszMyIWB4RF2bmTaXP+Zl5VUQcARxLVZR5NzA1Mz9ciitXRsQOwHpjjOPTwAWZ+c2ImEtVEHof8HbgpZl5YCnuPBgR/5SZd432eb0wr6TG1Jm2uOeee/Kxj30MCzCSNHHUyflLLrmET3ziE5SDW0nSBNBtxi9cuJC5c+ey5557EhFfHNbNrsDPM/OJ8vpyYM6wdeYAywAycyWwY0RMBfYDfpTPneKzDJgdEesAewHLR+izs6+HgMeB6eOM49lthrX/N7BpeT4VuBt4aKx95kwYSY3xG1JJarc6Ob/XXnux11578a1vfctiuyRNEN3m/Lve9S7e9a53ATBjxozDhy3enOrUoCGrSls364zWvinwWEdxprPP0bbZbIxxdG6zCnhpRLwwMy+OiB9HxFfLOl/JzMd+awd0sAgjqTEWYSSp3cx5SWq3HuX8fcBGHa+nlrZu1rkP2G5Y+83AA8AGERGlENPZ52h95RjjGNrml6X9F5n5dER8AFg3M99fTkf6YURcm5n/PtqH9XQkSY3xauqS1G7mvCS1W49yfhmwdUSsV17vBpwXEZuUU44AzqM6XYhyTZhrMnMVsATYKZ7reFdgcWY+BVwE7NLZ5wh9bQKsD1w32jiGbzOs/f8D7gHIzGeoTk9af6x95kwYSZIkSZLUiMx8NCIOBU6JiPuBFZm5NCJOorq+yonA54EFEXEc1cyXg8u2d0bEAuDkiFgNnFkuygtwCDA/IvYFtgKOKu3fBGZFxMdK+/szczUw4jjKNh8BPhMRrwS2pboYMMDngC+UvjYAbge+O9bn9RbVmjT8dq331vSWdj/96U9r58WrX/1qf5DNM+c1kMz53jPnJy1zXgPLrO8tc37tcyaMpMb4R1SS2s2cl6R2M+fr85owkiRJkiRJa4EzYSQ1xsq5JLWbOS9J7WbO12cRRlJjDG1JajdzXpLazZyvzyKMpMYY2pLUbua8JLWbOV+fRRhJjTG0JandzHlJajdzvj4vzCtJkiRJkvrqwgsv5LjjjiMi5jY9liZFZu3beo+n5x1KvWCVtvcyc4126s0339x1XixdupSLLrqIb3zjG2/NzEVr8r5aY+a8BpI533trM+eHbLfddv4gm2fOa2CZ9b1lzq99PZ8Js2jRIubNm8eiRf4/kqSxRUTXj3322YdPfepTWIBpnjkvqVt1ct5vSAeHOS+pW3Vyfugx2TkTRpOG/+B7b00r57fcckvtvNh22239QTbPnNdAMud7z5yftMx5DSyzvrfM+bXPC/NKaox/RCWp3cx5SWo3c74+izCSGmNoS1K7mfOS1G7mfH0WYSQ1xtCWpHYz5yWp3cz5+rxFtSRJkiRJ0lrgTBhJjbFyLkntZs5LUruZ8/VZhJHUGENbktrNnJekdjPn6/N0JEmSJEmSpLXAmTCSGtOrynlE7AO8A7gPyMw8Ydjy9YEFwF3A9sCJmXljWbY/MAtYDdySmaeX9mnA8cDNwDTgg5n5SMf7LQDOzMwvlraXAqcC1wBTy+OvM3N1Tz6kJE1AfkMqSe1mztdnEUZSY3oR2hGxIXAaMD0zn4iIhRGxd2Yu7VjtSOD2zDwpImYAZwF7RMSWwNHArMzMiFgeERdm5k2lz/mZeVVEHAEcCxwfEVOBlwA/GTaUFwGXZOZpZVyLgbcBC9f4Q0rSBGWxXZLazSJMfZ6OJKkxEVH7MYJdgZ9n5hPl9eXAnGHrzAGWAWTmSmDHUkzZD/hRZmZZbxkwOyLWAfYClg/vMzNXZea3hg8iM+8cKsAUU4BH6u0RSWqXXuR8R7H9/2Tmx4GZEbH3sNWGiu1/B5xMVWyno9h+dGYeA/xFRGxftjkNOL1scy1VsZ0uiu0nZuZHgG2piu2SNGnVyfcLLriAY489loiY2/S4m2QRRtLAO+ecc3jLW97CW97yFiJi3rDFmwMPd7xeVdq6WWe09k2BxzqKMyP1OaqI2KX0+4Nut5EkjcpiuyS1wJvf/GZOOukkMnNR02NpkkUYSY3ptmr+vve9j8WLF7N48WIy84xh3dwHbNTxempp62ad0dofADaI576SHanP0T7TDOBwYP+Og35JmpS6zfmvfe1r7L777sycOZOI+OKwbiy2S9KA6tHM9knFIoykxvQotJcBW0fEeuX1bsB5EbFJ+RYU4Dyqb1KHiiTXZOYqYAmwU0exZVdgcWY+BVwE7NLZZxefZxeqAsxfAhkRf1Rzl0hSq9Qptl9xxRVce+21ZObhw7qx2C5JA8oiTH0WYSQ1phehnZmPAocCp0TEJ4EV5aK8Hwb+qqz2eapCzXHAB4GDy7Z3Ul148eSI+BzVBRhvKtscAhxStpkBfKZj3B8AZgL7RcR7StuWwAXAq6m+Gb0A2Ll3e0uSJh6L7ZLUbhZh6os+FPD9RkADyX/wvZeZa7RT77333tp58bKXvcwfZPPMeQ0kc773BiXnI+LNwB8D9wNPZeYJEXES8FBmnhgRG1AV1e8BtgM+PezuSDtT3R3pxmF3R5oP/AzYCjiq4+5IHwAOpLrb0jmZ+Y1SbL8OWFH6egGwtFwsuG3MeQ0ss763BiXnJ5NaRZiobs1HZv5ijNUMbQ0kA7v31jS0//u////27j/W7vqu4/jzbdmFFlbD7SxDN1YYFcycuFhjlsW74I8MQm+XLLhEJ2SkelOaoAWZdEYJzMgKQS3GzO4yqIY/TLYxdbe3tk5HsoAsA/+YTJeOFQa6Cd0oWA2sHd3bP873lrNr749z7/fX/Z7nI/mGc77f8/3cz/mcc18l7/v9fj7PD5wX5513nh9khcx5rWTmfPnM+e4x57XSmfXlMufrN+/tSBHx0Yj4WPH4PcB/Ak9GxNa5zpmcnGTTpk2ntsnJ2XNoSlKPly82z5yXVCVzvnnmvKQqmfODm/dKmIj4K+C6zPx+RHwB+DPgb+nNm/DBOU6zcq5W8he+fMutnB85cmTgvFi/fr0fZInMeXWJOV8+c37lM+fVNWZ9ucz5+p2xwPGnisBeB1wKPJiZGRFP19A3SR3nP6KtYM5Lqow53wrmvKTKmPODW6gIs909OJUAAAuCSURBVL7477XAgb5l+NbO8XpJWjRDuxXMeUmVMedbwZyXVBlzfnALFWGeiIgjxeN3A0TEJ4DRSnslaSgY2q1gzkuqjDnfCua8pMqY84NbcHWkiHg9cBx4I3BOsfulzPzWHKd4D6layYAo33LvIX3hhRcGzot169b5QZbMnFdXmPPlM+e7wZxXl5j15aoz5w8cOMDBgwd54IEHtmTm1HJ+7kq20MS8dwAXZ+b7I+KzwNvprai0NzNvm+M0Q1utZGCXb7mhffTo0YHzYnR01A+yROa8usScL585v/KZ8+oas75c5nz95l2iGtgA/Frx+JHMvLDYt7HCPkkaEoMsZXfw4EFuvPFGImK86X53zAbMeUkVcenSVtiAOS+pIub84BYqwhzKzFeLx38NUEzm9fVKeyVJs1xxxRXs3r2bYb50sSLmvKRWOHDgADt27LDYXj5zXpJaZKEizExgk5nPnm6/JC2VlfNWMOclVWaQfL/yyiu55557LLaXz5yXVBn/f35wC62OdG5E/FhmfnNmR0RcgLOpSyqBIdwK5rykypjzrWDOS6qMOT+4hYowdwOfjYingeeAH6V3D+nmivslSaqHOS9J3WbOS1KLLGaJ6jOBq4CLgMPAdGaemOcUZ1NXK1mlLd9yZ1M/duzYwHmxdu1aP8iSmfPqCnO+fOZ8N5jz6hKzvlzmfP0WuhKGzDwOfKaGvkiSGmDOS1K3mfOS1B4LFmEkqSr+JUOSus2cl6RuKyvnI+KXgPcBR+gt4nb7rONn0bu98pvARmBXZn6tOPbrwDuAk8DhzPx4sX8D8Af0VoPbAPxOZv5vRPwQcAfwP8X++zLzi/P1IyJGgV3AU8XP/73MfL6vf5cCjwG/mpn75nuvFmEkSZIkSVIjImINsAd4W2Yej4gHI+IXM/Of+l62A3g2M++KiLcD9wE/HxFvAm4G3pGZGRGPRcTnM/PJos1bM/NLEXEDcAu9osz7gbWZubMornwxIn4COHOeftwB/GNmfjIixukVhK4p+r8a+F3gicW834WWqJakyriknSR1mzkvSd1WUs6/E3imuHUS4BF681j1uwp4FCAznwAui4i1wHuAf8nXJrt9FLgyIl4HXE7v6pTZbfa3dRT4LvC2Bfpx6pzT9O+PgD8E5ptr65TSizBTU1NMTEwwNTVVdtOSpBYw5yWp28x5SWW7//77GRsbY2xsjIiYmHV4Pb1bg2YcK/Yt5jVz7X8D8Epfcaa/zUHbmn3OMeDciDgjIq4FHs7Mp0/7xk+j9NuRxsfHGR8fL7tZSR3kXzxXJnNe0mKZ8yuTOS9psRab81u3bmXr1q0AnH322ZOzDh8BXt/3fG2xbzGvOQJcPGv/14HvAKsjIopCTH+bc7WV8/Rj5pyXiv0vZuarEXE5cCgidgIXAFdHxEhmzjkZurcjSZIkSZKkpjwKvCUiziyevwuYjojR4pYjgGl6twtRzAnz5cw8BhwEfiZeqwa9E/j7zPwe8BDws/1tnqatUeAs4N/m6sfsc/r3Z+Z1mbkrM3cBzwKfnq8AAxCvXZ1TmtIblMrgX+PKl5nLGtSXX3554LxYs2aNH2TzzHm1kjlfPnN+aJnzai2zvlxtyfmI+GXgauDbwPcy8/aIuAs4mpm7islv7wb+i96VL3fMWh1pE73Vkb42a3WkW+mtaHQBcFPf6kgfBV4u9t/btzrS/+tHsX8UuBN4BngrsHPW6kg3ATcADwN/kZn/PNf7twijoWFgl6/O0N6/fz/79+9n7969WzLTm9SbZc6rlcz58pnzQ8ucV2uZ9eVqSxFmmFiE0dAwsMu33NB+5ZVXBs6L1atX+0E2z5xXK5nz5TPnh5Y5r9Yy68tlztfPOWEkSZIkSZJqUPrqSJK0WP4lQ5K6zZyXpG4z5wfnlTCSJEmSJEk1sAgjSZIkSZJUA29HktQYL1+UpG4z5yWp28z5wXkljCRJkiRJUg0swkiSJEmSpEpNT0+zfft2ImK86b40KTIHXtZ7IaU3KJXBS+XKl5nLGtQTJ04MnBcjIyN+kM0z59VK5nz5zPmhZc6rtcz6cpnz9fNKGEmSJEmSpBo4Ma+kxviXDEnqNnNekrrNnB+cV8JIkiRJkiTVwCKMpMZExKK36elprr/++qGfyEuSVpJBcn5mkyStHOb84EovwkxNTTExMcHU1FTZTUsaYps3b2bPnj1kpuHSMHNeUhX27dvHtm3bLLa3gDkvSdVxdSQNDauu5VvubOonT54cOC9WrVrlB9k8c16tZM6Xz5wfWua8WsusL5c5Xz9vR5IkSZIkSaqBqyNJaox/yZCkbjPnJanbzPnBeSWMJEmSJElSDYa2CDM5Odl0FzrF8dRSOJu6qmQulcvx1FKY86qSuVQux1NLYc4PrvYiTBmzrJfRRhkhs9x+tGUsHE9JZTKXyju/TW10ZTwlLZ+5VG4bbegDdGc81V4zK68N+yp4Q1uEKUNXAtfxVFOsnHeTuVTe+W1qowxtGE/Vy5zvJnOp3Dba0IeydOm9aHEGyfctW7Zw7733kpnD/SFnZq0bMN6SNiaa7keLxsLxbF8bnRhPt+Hc/D1q5Vg4nu1ro/HxLKsNt+Hb2vLd68rvURv64Hg6nm71bVF8MJIkSZIkSarQ0E7MK0mSJEmSVCeLMJIkSZIkSTWwCCNJkiRJklSDM5ruQNUi4nzgZuAlYBVwCfAN4G+Au4AR4B+Kl48CT2Xm7vp72ryIGAM+AlwIbMzME33H7gSuAW4FDgG/CRwGzgU2Au8FLqI3pj8OfLKv6UuB1cCTwOuA3wL+uDi2MTPfW927are5vp+Z+eGI2AZcBjxPb2y/lZk7G+us1FLm/OKZ8/Uz56XlM+cXz5yvnzmvgTU9M3CVG3AW8Bjw5r59I8Cni8e3AXf3HVsHvKvpfjc8ZrcBXwK29+1bDzwEPF48fxz4qb7jHwNGiscfnBnfvuO/AlxXPD6n97U7dey6pt9zg2M95/cTWAt8G05Nnn0GsKfpPru5tW0z55c0ZuZ8fWNtzru5LXMz55c0ZuZ8fWNtzrsNvHX9dqSr6FUh/2NmR2aeyMyrZ78wIt4I3JCZj9TZwZb6CLAzIs4snm+nF8wzngNuioiLATJze/ZV2ftFxJ9n5qcyc+/pjs+1f0jM9/08DgS9cX5DZr6amdua6qjUYub80pjz9TDnpeUz55fGnK+HOa+Bdb0IcxG9gAEgIt4SETsjYioiNhS7L4+I3fSCSj1fAR4FJiLiPOD79Kq4M64tnn8uIg5FxG/POv+yiNhdjOsP19LjlWnO7ydwPvBu4KeBr0bEwxFxRUP9lNrMnF8ac74e5ry0fOb80pjz9TDnNbCuzwlzGPi5mSeZ+QywKyK+Qe8yOoCHMvPmiBgB3lR/F1vrduAgcAFwJ/CTMwcy8yjwIeBDEbEJeDAiDmfmvuIlX87MHQARcUm93V5R5v1+ZuZXgGsiYhXwPuAzEfHmzHyhkd5K7WTOL505Xz1zXlo+c37pzPnqmfMaWNevhJkGLuyrkhMRQW/CpB9QXH73XET8Qm29a7HM/HfgC8CJzPxO/7GI+FwxjmTm48C/0rv38XTtHKq6ryvYvN/PiLgPIDNP0pt47rv1d1FqPXN+icz5Wpjz0vKZ80tkztfCnNfAOn0lTGYej4jNwC0R8SK9X4aLgb+kN2nXGDASEb9fnLIGOAF8voHuNq6ogo8B50TEhzPzA8X+H6E3k/r5EfEB4KvA/RHxDL0Z6J8G/q64p3QcuCQifiMzP3GaHzNTUb8pM/+k+nfVXgt8P18E1kXEnwL/TW+G+1usmks/yJwfjDlfL3NeWj5zfjDmfL3MeS3FzEzNkiRJkiRJqlDXb0eSJEmSJElqBYswkiRJkiRJNbAII0mSJEmSVAOLMJIkSZIkSTWwCCNJkiRJklQDizCSJEmSJEk1sAgjSZIkSZJUg/8DE227E49TOn8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1382.4x345.6 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# second order statistics overview\n",
    "summary_second_order = second_order(returns, rhos, log=True, render=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Third Order\n",
    "\n",
    "Test percentage deviations of third order moments. Perform Skewness test to check deviations from normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def third_order(df_1, df_2, log=False, render=False):\n",
    "    \"\"\"Third order moments comparison test.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    df_1: pandas.DataFrame\n",
    "        Family 1 data.\n",
    "    df_2: pandas.DataFrame\n",
    "        Family 2 data.\n",
    "    log: bool, optional\n",
    "        Flag for printing summary.\n",
    "    render: bool, optional\n",
    "        Flag for printing distributions.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    summary: dict\n",
    "        Third order test summary.\n",
    "    \"\"\"\n",
    "    # type check\n",
    "    assert isinstance(df_1, pd.DataFrame)\n",
    "    assert isinstance(df_2, pd.DataFrame)\n",
    "    # interface check\n",
    "    assert (df_1.columns == df_2.columns).all()\n",
    "    # column-wise skewness values\n",
    "    skew_1 = df_1.skew(axis=0)\n",
    "    skew_2 = df_2.skew(axis=0)\n",
    "    # relative deviation of skewness values\n",
    "    dskew = np.abs((skew_1+eps) / (skew_2+eps) - 1)\n",
    "    # summary table for each ticker\n",
    "    summary = {}\n",
    "    # iterate over tickers\n",
    "    for ticker in df_1:\n",
    "        # Skewness test for family 1\n",
    "        skewtest_s_1, skewtest_p_1 = scipy.stats.skewtest(df_1[ticker])\n",
    "        # Skewness test for family 2\n",
    "        skewtest_s_2, skewtest_p_2 = scipy.stats.skewtest(df_2[ticker])\n",
    "        # summary table columns\n",
    "        _name1 = 'series 1'\n",
    "        _name2 = 'series 2'\n",
    "        _name3 = 'deviations'\n",
    "        summary[ticker] = pd.DataFrame({_name1: [skew_1[ticker], skewtest_s_1, skewtest_p_1],\n",
    "                                        _name2: [skew_2[ticker], skewtest_s_2, skewtest_p_2],\n",
    "                                        _name3: [dskew[ticker], None, None]},\n",
    "                                       columns=[_name1, _name2, _name3],\n",
    "                                       index=['skewness', 'skewtest statistic', 'skewtest p-value (>0.05)'])\n",
    "    # print summary\n",
    "    if log:\n",
    "        for ticker, summ in summary.items():\n",
    "            print(ticker)\n",
    "            print(summ)\n",
    "            print('\\n')\n",
    "    # plot distributions\n",
    "    if render:\n",
    "        # random selection of assets to render\n",
    "        I = np.sort(np.random.choice(df_1.shape[1], min(3, df_1.shape[1]), replace=False))\n",
    "        # initialize figure & axes\n",
    "        fig, axes = plt.subplots(ncols=len(I), nrows=2, sharex=True, figsize=(6.4 * len(I), 4.8))\n",
    "        for i, m in enumerate(I):\n",
    "            # normal distribution with family 1's statistics\n",
    "            mu_1 = df_1.iloc[:, m].mean()\n",
    "            std_1 = df_1.iloc[:, m].std()\n",
    "            norm_1 = np.random.normal(mu_1, std_1, 10*df_1.iloc[:, m].count())\n",
    "            sns.distplot(norm_1, label='$\\mathcal{N}(%.4f, %.4f)$' %\n",
    "                         (mu_1, std_1), color='k', norm_hist=True, ax=axes[0, i])\n",
    "            # distribution plot of family 1\n",
    "            sns.distplot(df_1.iloc[:, m], label='%s' %\n",
    "                         df_1.columns[m], color='g', norm_hist=True, ax=axes[0, i])\n",
    "            # normal distribution with family 2's statistics\n",
    "            mu_2 = df_2.iloc[:, m].mean()\n",
    "            std_2 = df_2.iloc[:, m].std()\n",
    "            norm_2 = np.random.normal(mu_2, std_2, 10*df_2.iloc[:, m].count())\n",
    "            sns.distplot(norm_2, label='$\\mathcal{N}(%.4f, %.4f)$' %\n",
    "                         (mu_2, std_2), color='k', norm_hist=True, ax=axes[1, i])\n",
    "            # distribution plot of family 2\n",
    "            sns.distplot(df_2.iloc[:, m], label='%s' %\n",
    "                         df_1.columns[m], color='r', norm_hist=True, ax=axes[1, i])\n",
    "            # y-axis limits\n",
    "            ymin_1, ymax_1 = axes[0, i].get_ylim()\n",
    "            ymin_2, ymax_2 = axes[1, i].get_ylim()\n",
    "            ymin = min(ymin_1, ymin_2)\n",
    "            ymax = max(ymax_1, ymax_2)\n",
    "            # setting for family 1\n",
    "            axes[0, i].set(ylabel='Frequency', xlabel='', ylim=[ymin, ymax])\n",
    "            axes[0, i].legend()\n",
    "            # settings for family 2\n",
    "            axes[1, i].set(ylabel='Frequency', xlabel='', ylim=[ymin, ymax])\n",
    "            axes[1, i].legend()\n",
    "        # present figure\n",
    "        fig.show()\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL\n",
      "                          series 1  series 2  deviations\n",
      "skewness                 -0.101652 -0.262711    0.613064\n",
      "skewtest statistic       -1.851610 -4.719285         NaN\n",
      "skewtest p-value (>0.05)  0.064082  0.000002         NaN\n",
      "\n",
      "\n",
      "GE\n",
      "                          series 1  series 2  deviations\n",
      "skewness                  0.224004  0.092329    1.426143\n",
      "skewtest statistic        4.041581  1.682532         NaN\n",
      "skewtest p-value (>0.05)  0.000053  0.092466         NaN\n",
      "\n",
      "\n",
      "JPM\n",
      "                          series 1  series 2  deviations\n",
      "skewness                 -0.024998 -0.162936    0.846578\n",
      "skewtest statistic       -0.456425 -2.956270         NaN\n",
      "skewtest p-value (>0.05)  0.648084  0.003114         NaN\n",
      "\n",
      "\n",
      "MSFT\n",
      "                          series 1  series 2  deviations\n",
      "skewness                  0.095391 -0.107883    1.884208\n",
      "skewtest statistic        1.738092 -1.964486         NaN\n",
      "skewtest p-value (>0.05)  0.082195  0.049474         NaN\n",
      "\n",
      "\n",
      "GS\n",
      "                              series 1      series 2  deviations\n",
      "skewness                 -4.323192e-01 -5.988842e-01    0.278126\n",
      "skewtest statistic       -7.569613e+00 -1.014563e+01         NaN\n",
      "skewtest p-value (>0.05)  3.743370e-14  3.465198e-24         NaN\n",
      "\n",
      "\n",
      "MMM\n",
      "                              series 1      series 2  deviations\n",
      "skewness                 -3.754764e-01 -4.824344e-01    0.221705\n",
      "skewtest statistic       -6.638096e+00 -8.369236e+00         NaN\n",
      "skewtest p-value (>0.05)  3.177607e-11  5.799276e-17         NaN\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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gObtP7ubvOX9n490b3eqIS57vPsnzfc/QoUP1nDlzpKgu/GbhuoX8af2fePFbL2I0GNuOO7SDX2X9igXTF/DMNc+41Zbk+e7rj3m+R04jEsIfGiwNnG483e56LS4hASEkhyWzqXiTjyLznUmTJjFnzhzsdru/QznLmDFjenRitlgsfOc732k3MQsheg5X7r7QTkQurt8DrqmlfYnk+a6RPN95siyA8LfCmkJSwlPOKrSAc7poYmgihyp61zQid0me7xpv5HmZRiREK9eq5KnhF19saWDUwD7ZCQe4++67/R1Cr2M2m/nhD3/o7zCEEG7YVLSJkICQ8xZMPFNUUBTh5nDJ86KN5Hkhep/C6kIGRg684GdJYUm9bs2WzpA833neyPM9cmSL7EYk/OFk/UmADjvgLukR6ZysP9l2jRBCiN5hU/EmMqIyMKj2u0BKKdIj0/tssUUIIfq6RdsXsa9sH1a79YKfJ4UlkVeVR7Ot+YKfC+EJPbLYIsMOhT+U1pcSYAg4bxGtC0mPcO5WtPvkbm+HJYQQwkNqW2rZV7aPQVHtTyFyGRg5kP3l+6UjLoQQvZBDO6hqriImJOaCnyeFJeHQDo5WHvVxZKI/6ZHFFiH84WT9SRLDEjt82umSFpEGwK6Tu7wdlhB9koxgFP6wtWQrGt3hei0uAyIHYHPY2Fe2zweRCSGE8KTq5moc2kFs8IUforq2fz5YftCXYYl+RtZsEaLVyfqTZEZlunVuqDmUgZED2XVKii1CdIVrBKMQvrSpyLk4bmb0xXP9mYvkTknpl5vJCCFEr1XZVAlATPCFR7YkhiWiUH163Rbhf1JsEQKw2C1UNFYwM22m29dMSJrQpZEtrm2mPaWjbaqFEEJ8bXPJZkbFjyIkIOSi58YGxxIVFNWldVskzwshhH9drNhiNpoZGDWwyzsSSZ4X7uiRxRbX8PK5c+fKui3CJ07Vn0Kj24YUumN84ng+OvwRDZYGQs2hXozOM0pLS3n22WeJiorCbrdz+PBhMjIyuOmmm7j//vuxWCxcc801AFRWVjJo0CAWLFjg56hFT6WUMgArgS2AGRgM3A0EA08BecBQ4CGt9Sl/xSmEi9aazcWbuWnETW6dr5RiUvKkXrNIbnZ2NgsXLiQ/P58jR45gNpvbPnvggQdYsmQJjz/+OMOHD+cf//gHgwcPpqqqiiNHjvDhhx+Sl5fH/fffT25uLrfeemvbtYcOHaKpqYmhQ4ditVr561//yu9//3uAtmtF36aUCsaZ6z/VWt+nlAoCngVKcOb5p7TWuf6MUYhzVTVVAe0XWwBGxI3oVdOI3MnzP/3pT8nLy2PJkiXs3buXUaNGtZ2TlZXFddddxx/+8Acef/xx/ud//odHHnmEl19+mZ/+9Kdt550+fZrMzEzmzp3L448/zpAhQ3z6ffYlPbLYIsPLha+5tn12ZycilwlJE9Bo9pXtY3radG+F5hHNzc3ceOONfPDBB6SnOxf3tVgsfO9732PatGlcfvnl1NfX8+ijjwJQUVHBoUMyrFJc1Cat9RMASqkPgZuBy4DPtNbvKaXm4uyQ3+HHGIUAILcil8qmSmamzcSu7W5dMylpEn/b+jesdisBxgAvR9g9s2fP5vLLL6exsZHFixfz85//HICysjK2bt1KSkoK9957L1OmTOG1115j3LhxAPz85z/H4XAwbNgwvvOd77Bq1aq23wUA77//PvX19dx1113U19fz17/+lWeffRaAf/7znz7/PoVfPAHsPOP9AuC41vrPSqmxwKs4c78QPUZVcxVBpiCCTEHtnjMidgTZhdk4tMOtNRv9zZ08v3DhQr788ksOHDjAo48+ynvvvdd2/erVqwF46KGHMJlMLFy4kI8++ognn3ySu+66i8DAQABeeuklBgwYwPe+9z0ptHRTz/+pEsIHTtafRKFICE1w+5oJSRMA2H2q5+9ItHr1ajIyMtoKLeDcS3758uXnnXvy5En+9re/cemll/oyRNHLaK0dZxRaTEAacBi4HtjUetrG1vdC+N2mYueP5Yy0GW5fMyl5Ei32FvaX7/dWWB63cOFCnnrqKVpaWgD4+9//3tYhB0hKSuK5557j6NGjbZ+f+XT0TL/85S+55ZZbuOuuuy74eXvHRd+hlLoDZy7PP+NwW57XWu8FxiulIvwQnhDtqm6uJjoousNzRsaPpNHaSFFNkY+i8oyL5XmAe+65hy1btrB3714AVq1axfXXn98lGzNmDNOnT2fx4sWAs3CjlCI+Pt7L30X/4JVii1LKoJRarZRaqJR6Qin1rlIqWCkVo5RapJR6UCn1qlIq0Rv3F6KzSutLiQ+J79STy4yoDCICI3rFjkR5eXkkJX09aqewsJCnnnqKuXPnUlBQAMC6detYsGABCxcu9FOUojdSSl0LrAJWaa1zgASgrvXjWiC6tRhzlvLycqZMmdL2ktGMwts2FW0iMjCSkfEj3b5mWuo0ALYUb/FWWB43ZswYZs6cyaJFizh16hQGg+GsTvObb75JfHw8V199NcOHD+eFF1446/rdu3ezYMECFixYQE1Nja/DFz2IUmoUMFJr/cE5H52Z58GZ6897WiV5XvhTVXMVUUFRHZ4zIm4EQK9bJPdieR4gMDCQBx98sG2kYlZWFt/61rcu2N4jjzzC008/TXNzMy+99NJ5hRvRdd6cRiTDy0WvcbL+JEnh7k8hAud8/rEJY9lbttdLUXnO4MGD2bLl638sDBw4kAcffJCMjAzq6+sBmDNnDs8++ywWi4Xi4mJ/hSp6Ga31J8AnSqk3lVI/B8qAcKAaiACqtNa2c6+Lj48nJyfHt8GKfm1T8Samp03v1FDxQdGDiAuJY0vJFn4y5SdejM6zHnnkEa699lqOHz/OAw88wL59X29fHRMTwzPPPMMzzzxDTk4O8+bNY/Dgwdxwww0AjB8/nueffx6Aw4cP+yV+0WPcBDQrpR4EZgFmpdQCvs7zLhGtx84ieV74U3VzNclxHa/FODLOWXw/ePog1w651hdheUxHed7lnnvu4emnn+axxx7jxhtvbLet0aNHc8kll/DYY48RHBxMXFycN0PvV7wyskWGl4vexKEdlDWUkRjauYFWi7Yvwmgwsv3Edl7JecVL0XnG9ddfT35+ftsoFnAuFmm3n79ugdlsJikpiS+++MKHEYreRik1Sil1Zg7PBwYBqwHXtl6Xtr4Xwq9qW2rZV7avUzvOgbOoPi11GltKes/IFoBRo0Yxe/ZszGbzeZ3mq6++Gq01AFOmTGHcuHFYLJYLtjN8+HCvxyp6Lq31n7TWj2utnwI2AFu11s9zRp5vXbNlt9a61o+hCnEWm8NGTXMN0cEdTyP64OAHhAaE8q+D//JRZJ7TUZ53MZvNPPTQQ6xdu5Zrr+24mPTII4+waNEifvGLX3gj3H7LrZEtSqnZWuvszjbeOrz8t7QOL1dKXXB4+blPPV3DDl3mz5/P/PmyHZbwjqKaImwOW6eLLQCp4alk27Kpaq5y+xp/bO0WGBjIqlWrePrpp4mOjsZut3P06FF+9KMfUVFRQXZ2NhaLhSeeeAKAxsZGzGYzV1xxhc9jFf7RhTzfAtyjlJoIBAAjgV8DFuBppdQwnDsU3efxYIXopK0lW9HoDost2eudP/6zL5t91vHpqdPJOpJFbUstEYHuLUvhjzyfk5NDdnY29fX1PPnkk7z99tuAs0+1ZMkSSktLefvttxk5ciR33303AwcOpLKykszMTL797W9z9OhRVq5cyeHDh1m8eDH33nvvefdwjXh57rnn+N3vfufT7090Xzf68/OA2ThHttwOvAA8q5R6GBgC3OPZSIXoHtcuoxebRqSUIiksiZP1Jzt9j56a59944w2ys7Oprq5m1qxZZ/07+rnnngPg2Wef5dFHH+W5555jz549fP7551x55ZVUVFQAsGLFCgoLC1m2bBnjx48/a81H0TnuTiP6i1JqObBYa13hbuMyvFz0BkcqjwBccHFca4uV+pp6gkODCQo9fzXz1PBUAEpqS7wbpAckJye3dZTPJaNYBJ3M81rrYzinh17Ijz0amRBdtGi7c42I1UecA6wOnj5IYU1hp9qYnjodjWZbyTauHHSlx2P0lClTplwwl8fHx/Pqq6+2vf/+979/weuHDBnCihUrOrzHww8/zMMPP9y9QIU/dbU/vwI494dDHn+LHqu41jkd/mIL5IJzJ9I9p/Z4OySPcDfP33nnnRe8/ne/+91ZhfJz37vMmzePefPmeSBi4W6x5ddACfAbpVQw8Gbr6uMX1LqgVqbW2jV8/Nzh5UXI8HLRQ+RW5AKQGJbY9mSz5mQNx/9znAObD2CzOuuB4XHhpI5JZfhlw7nu29cBkBKeAkBJXc8vtghxEZ3K80L0JnlVeSSHJRMSEHLeZw6Hgy0fb+GT1z+hqbaJfWP3ce2d1zJ43GDgjEVyS7b06GKLEG6QPC/6BVe//GIjW8BZbNlYtJHKpkpigmO8HZroZ9wtthRprYuVUl8BDwDvtH69TGu9/gLny/By0WvkVuQSaAwkMjASgNLDpXy5+EuCgoO4/NbLqbXU0tLQQnlBObnrc8ldn0u0MZqZN8wk1BxKdFC0FFtEX9DZPN8tNTU1zJ8/n7lz5zJ37lxPNy9EG601+VX5TEiacN5n9dX1LH5oMQe3HiQyKZL4zHjy9uTxzD3P8K27v4XjJw4MBgOJoYm8t/894kLi/DJ0XAgP8WmeF8JfOjOyJTnMuYjuodOHuCT9Eq/GJfofd4stbymlooEC4Gmt9ecASqnngfOSswwvF73JkcojJIQmoJSi5lQNX736FZEJkVzx0ysICg8ika/XcmmsbmTTO5t4/dHXMQWYmHrtVFLDUzlRe6LDe2itUUp5+1sRPZRrMcoerlN5vrsiIyNlG1DhE6caTtFgbWBQ9KC2Y9nrs6k7XccX//cFzXXNfP8P30fHO/P0tMnTWPbMMj5+9WMqTlRw5yN3khmVyYHTBzr8uyx5vn+TPC9Ez1FSW4LJYCLMHHbRc5PCnLuRulNskTzfv3Ulz7tbbGkBbm4togCglDID8e1fIkTvkFuRS0JoAlprtr6/FWOAkTk/mUNQ+PlrtIREhTDnJ3NY++JaXn/8dcqby0kZlsLB0wex2q0EGAPOuyYoKIiKigpiY2MlQfdDWmsqKioICjr/56mHkTwv+qS8qjyAs4otDVUNfPbSZ9gsNq78+ZWQAApnft66fSsZczJosDew5eMtNNQ0MPo3o9lcspnKpsoL3kPyfP8meV6InqW4rpiooCi38nFsSCwmg4mD5Qc7PE/yfP/W1TzvbrHlj4ADoHUK0DGttQW48Cpr3STDy4WvWOwW8qvyuXbItez6chdlx8qYdss0giOC273GYDRw6R2XsurpVexatYsrHrkCu7aTW5HL6ITR552flpZGcXEx5eXl3vxWRA8WFBREWlqav8O4GJ/meSF8pbCmkEBjYNvTy5amFr5a/BXWZitX/fIqYlLPn6OvlGLM1WMIDA1k6/tbCcgMgKGQX51/wXtInheS54XoOUpqS9xarwXAoJxTRQ9VHOrwPMnzoit53t1iyx9wbvOWDyTj3OLtgU7dqRNkeLnwlfyqfOzaTkJoAp8u+ZSwmDAGTx980evCYsIY8Y0R7F+7H2OFEYC9ZXsvWGwJCAggMzPT47EL4WE+zfNC+EpxbTFpEWkYlAGA9597n6rSKi7/8eUXLLScaeglQ6k7XcfOpTsx/n/GdostkudFLyF5XvQLxbXF7S52W1dVR0VpBTGJMUTERgDOqUSHTndcbJE8L7rC4OZ5m7XW2QBa66+AKu+FJITvuLZ9tp2wkbcnjxGXj8BgdO+vxag5ozAHm9mxbAcGZWDvKVnQX/RqPs3zrhGMK1eu9OZtRD/n0A6KaopIj0gHYP+m/az/13pGzRlF6shUt9oYf914IuMj0Sc0uwp2eTNcIbxN8rzo87TWlNSdPbIle302q/+1msfueIz7rr6PJ3/4JPd/836e/c2z2G12ksKSyKvKo9nW7MfIRV/k7siWDKWUSWttU0qZgAHeDEoIX3Ft+3zo00MEhQYxeNrFR7W4mIPNDJ4xmF2f7yLuijj2lkmxRfRqPs3zMoJR+EJ5Qzkt9hbSI9P7O1qJAAAgAElEQVSxWW28/t+vEx4fzrhvjXO7DaPJyNT/mspnRz6jIqmi3fW5hOgFJM+LPq+yqZJmW/NZxZZTR0/x5eIvUUox9pqxRKdFczL3JLkbcnnxoReJvjYah3ZwpOIIYxPH+jF60de4O7LlEyBfKbULyAOyvBeSEL6TW5FLdFA0uz7eReqYVEyB7tYfnYbNGobWGkOFQYotoreTPC/6HNf2n+kR6axbto7aslomf2cyRpOxU+0kDk4k0haJNmhyinK8EaoQviB5XvR5JXUlwNfbPufvy2fdonWERoVy/f3XM+5b40gfm87UeVMZMmMIB9YdwFTj7P/vObXHb3GLvsmtYovW+iNgLHAvME5r7dXxgDLsUPjKkcojxOgYbBYbg6YNuvgF5wiLCWPCNyZQsauCguoC6lrqvBClEN7n6zwvhC8crz2OQRkIs4ax6h+rSBmZQuoo96YPnWv0COeaXK+sfsWTIQrhM5LnRX/gKrJHBUdRX13Py/e9TFB4EFf+4kpCo0PPOnfcdeMwGA0Urysm0BjIrpMyVVR4lrsjWwAUUAZEKKUe9U44Tq5hh7ITkfCWRdsXsWj7InaW7uR03mnCYsKIz+zazoexo2OxHrcCsL98vyfDFMLXfJbnhfCFopoiksOSWfv6WizNFiZ9e1KX2xo4aCCqUZG1RwYDiF5N8rzo00pqvx7Z8tf/91fqquqYfddsgsPP32k0ODyYQVMGUZBTwKi4Uew+tdvX4Yo+zq1ii1LqVWA98DrwBnCHF2MSwicsdgtVzVXU5tYyYMIAlFJdaidhUAIROFczl0VyRW8leV70RUW1RSQFJbHh3xuYcd0MIhMju9yWwWAg3BJOWUAZBw8e9GCUQviG5HnRHxTXFqNQnDpwisKdhYy5egwxae3vPJcxOQO71U5jeSObizfzSo6MXhSe4+7Ilkit9Rit9RVa6zk4t4oTolcraygDQJ/WDJw4sMvtKKUYOmootMC6A+s8FZ4QvubTPC/TRYW3naw/SW1LLQ2HG7BZbFx757XdbjMtKg3i4bd/+K0HIhTC56Q/L/q8kroSEsMS+eiljwiODGbknJEdnh8/KJ6g8CAachuos9RR01Ljo0hFf+BusWW/UirsjPfR3ghGCF86VX8KgOiAaKJTu/cjnTk5E1WuWH94vSdCE8IffJrnZbqo8LadpTsBOLLmCGlj08gtyu12m0khSQBsO7Gt220J4QfSnxd9XnFtMZEqkrw9eYy+ajQmc8ebXxgMBgaMH0DpplLAOf1UCE9xt9hyF1CmlMpXSuUDi70YkxA+cfz0cQCmTJ7S5SlELoGhgWSEZFBiK6GlpcUT4Qnha5LnRZ/iWujQftzO6KtGe6TNWFMsAJXBlTKVSPRGkudFn1dcW0x1UTXh0eEMnjbYrWsGTBiAvdjuvL6u2JvhiX7G3WLLO1rrEK11ptY6E7jfm0HJ8HLhC7l5uVALM66a4ZH2BpgHoIM19/xWRuWKXsmneV4Ib1u+cTmqRpGYlkhseqxH2jQrM/EqHlJg+fLlHmlTCB+SPC/6NK01BVUFnDp8ilk3zbroqBaX+Mx4IsMjMTeZZWSL8Ch3t35+UCllUErFKaWU1vpVbwYlw8uFLxRVFWGsM3Ks9JhH2puWPg2ADcc2eKQ9IXzJ13leCG/La8pDn9AMnu7ek013DQoahHGAUYototeRPC/6upqWGhpsDVADgWmBbl9nMBiYOGci1uNWKbYIj3J3N6JrgDzgNeB2pdRPvBqVEF7WUNuANcRKuCG821OIXNLN6aCh0FrI6dOnPdKmEL4ieV70JXUtdVQbqjGUG0gfm+7RtjMCMrCH2NmTv4fc3O6vAyOEr/g6z8tIdeFrT7/yNAAZURmERod26lpjvBF9QlPWWEaDpcEb4Yl+yN1pRHOBEcBGrfU7QIr3QhLC+7Zv2A6hkBCZ4LE2tzRvIcwRBknw2+dlpwrR6/g0z0snXHjTpvxNACSEJGAKdG8YubsGBrTuXpcKK1as8GjbQniZT/O8jFQXvrbtsHPx8hkjO79EQMKgBExVzt8Xe8v2ejQu0X+5W2wp1lo3A7r1vawAKnq17Tu2A5AUk+TRduOC4lCpipy1OR5tVwgf8Gmel0648KYla5cAMHTQUI+3nR6QjgEDyVOS+fDDDz3evhBeJP150aftzHfuQteS3PkfbYPRQHJUMgA5xdKPF57hbrFlmFLqQWCUUuqXQJoXYxLCq1paWjhafBSACFOER9uONcaiwzSHDh7i1KlTHm1bCC+TPC/6jM8PfI5qUqQN9PyPsVmZSTWlEjI0hC1btnDixAmP30MIL5E8L/qsnJwcKu2VKIci2BDcpTYyh2VCE3y882MPRyf6K3eLLQuACCAOSEJ2IxK92Lp167BF2EBDmCHMo21HG6OdXyTBBx984NG2hfCyTuV5pdRgpdS7Sqn/p5R6QSm1sPV4jFJqkVLqQaXUq0qpRO+HLsTXysrKKNWlxFpiMRjc7eZ0zsCAgZQ4SgC4/37Z0EX0Gj7tzwvhS2+88QYqShFqCO3yeowpw1JQpxQbjshmF8Iz3JrIrLWuAx5yvVdKjQAOeSso1/ByITxt0aJFvPPOO5AM4YZwjMro0fZjjDEAhI0KY9myZfzsZz/zaPtCeEsX8nwMsFRr/WHr+QeUUquBHwOfaa3fU0rNBZ4F7vBe5EKcbfm/lkM8DDV4fgqRS4Y5gw1NG4gZEsOuXbu8dh8hPMnX/XkhfMVms7Fs2TJCbg8h1Ni5hXHPZAwwEmYLoy6kjhZrC4EB7u9oJMSFuFVscT2xPMNs4KoOzh8MPAHswDlEsUJr/bhSKgZ4CudK6EOBh7TWMtdC+IzWmt27d2OabCLSGOnx9gNUAOGGcEJGhJD952xOnDhBSoqsJy16vs7mea31tnMOGYAG4HrgT63HNgJveCpGIdzx1pq3YAKMihxFM81euUdGQAYAKVNSOLT8ELW1tUREeHZaqhCe1tk8L0Rv8fnnn1NeXk5oZCihhq4XWwCSI5KpC6hj6WdLufNbd3ooQtFfuTu+diJQ2PrSXLwK7nri+YzW+jfAbUqpycD/4Hzi+RTwb5xPPIXwmcLCQqprq7GH24kweqdjHGuMpTqoGq01y5cv98o9hPCCzub5Nkqpm4BPtNaHgASgrvWjWiBaKXVeYb+8vJwpU6a0vWQ0o/CEqqoqthzfAsAA8wCv3CO7MZtjlmMYMVI7sBabzcaaNWu8ci8hPKzLeV6Inuydd94hIjqCRmNjt4stmamZzjbXveOJ0EQ/5+5+iPO11uWuN62LarVLnniKnmr37t0QDVppIgzeKbbEGeMoUAUkDk/kvffe49e//rVX7iOEh3Uqz59x3hxgDs61AADKgHCgGufaAFVaa9u518XHx5OTI6v9C8/66KOPcCQ4CDIGkWBMIJdcr9zHoAzEGGNoDG0kMCyQFStWcOutt3rlXkJ4UJfyvBA9WVNTE8uWLWP07NHsYEe3iy0xwTGoJsWm/E1orbu8/osQ4P7IlpFKqdmtrxuAq929gTzxFD3J7t27SR7r3NYt0uD5aUQA8aZ4ANJmpLFx40aKioq8ch8hPKzTeV4pdT1wLfAbIEkpNRNYDcxsPeXS1vdC+MSKFSsIHBjIhKQJGJR3Fsd1iTfFU+moJG1iGitXrqS+vt6r9xPCA7rcnxeip/rss89oaWkhfVw64FyTsTsMykCcLY66sDq2b9/uiRBFP+ZuT+QF4EfAXcA1ONdjuagznnj+tvWQ64knuPHE0/WaP3++m2EK0b5jx45RUlJC3Ig4AK9NI4oyRGHESEBmAADvv/++V+4jhId1Ks+3Tg1dBswA1gEfAsNxLr54tVLqYeBm4D7vhSzE1+rr6/k462Ns8TYCq7y/qGG8MR4HDqImR9HU1CQ7KIreoEv9eSF6sqysLAIDAzEnmYHuF1sAhoYPhWR49LFHu92W6N/cnUb0U631ls403PrE8zKcTzyTlVID+fqJZxHyxFP42IoVKwAISA4giCDMyuyV+xiUgVhjLKWGUiZOnMiyZcv43e9+55V7CeFBncrzWuvtQHt7p//YMyEJ4b77X7kfe7gdTNCkm8huzPbq/eKNzlGMtgQbqampvPvuu9x+++1evacQ3dTp/nx31NTUMH/+fObOncvcuXN9dVvRj2itycrKYsSIEVRSSaAKJEgFdbtdu8kOgfCfwv94IErRn7k7suUWpVS6UmrAOa+nLnSyPPEUPdEHH3zAgAEDqDZVe20KkUu8KZ4iaxHzvjuPrVu3kp+f79X7CeEBncrzQvQ0ezfuxZDu7NbEGGK8fr9AQyCRhkjKHeV897vfZc2aNVRVVXn9vkJ0g0/zfGRkJIsWLZJCi/Ca3NxcCgoKGD16NGW2MuKN8R5ZYyXG6PwdUuWokrwuusXdYss3gfXAktb//gvn4ra3XOhkrfV2rXWY1vry1tdUrfXrWutKrfWPtdZPaK3vkm2fha8UFxezZcsWJk6ayEnbSa9NIXKJM8bhwEGhtRCA3//+9169nxAe0Kk8312uJ54y9UJ4gtaafRv3ETYqDIUi0ujdgrpLvCmecls55kAzVquVDz74wCf3FaKLfJrnhfA2105wo0eP5rT9NHHGOI+0G2mIRGkFyfDFF194pE3RP7lbbFkGZGqtvwFk4tzWeQ7wK69FJoQHuTrAwyYOo1E3em0nIhdXsj8dcprMzEzZdUX0Bj7N8/LEU3jSwoULqS6rxpBmIMIQgen8tfe9It4YjxUrASkBJCQk8M47slWo6NGkPy/6lKysLIYPH05MbAzltnISTAkeadeojEQZo1Apik8++cQjbYr+yd1iS6TWWgNorR1ASuvXH3sjKHniKTztgw8+YPTo0TRFNQFfDw/0liBDEEnGJI5YjjBlyhSKiorIzfXOFqRCeIhP87wQnrR3714AmsOaiTZG++y+ro79Mesxpk2bxrp162QHOtGTSZ4XfUZjYyOff/45aWlpVDuqsWFrW0vLE2KMMahUxZpP1tD610aITnO32JKolHpJKbVAKfV3wHM/yRcgTzyFJ5WVlbF+/XrmzZvHcetxFMonnfFhgcM4ajnKxEkTAVi2bJnX7ylEN/g0zwvhSXv37iVqaBTNNHu9mH6mUBVKsArmqOUoM2bMQGvN22+/7bP7C9FJkudFn/HVV19hs9kYPXo05bZywDm101OijdE4Ah0UVcsDU9F17hZb7gH2A0Nb/3uP1yISwsPeffddHA4HSimOW4+TYEwgQAV4/b5DzUNp1s3Uh9czZMgQKbaInk7yvOiVysvLyc/PJ2piFIBPR7YopUgwJXDYcpi4uDhmzZrFm2++KU9BRU8leV70GVlZWQQEBDBs2DBKbaUAJJmSPNZ+W+E+GZlKJLrMrWKL1toCvAe8DLyhtW7xalRCeIjWmtdee42MjAxSUlIotBYyIGCAT+5daa8EYE39GqLGR7F//3727dvnk3sL0VmS50VvtWaNc4i3OdMMQLTBd8UWgGRTMjWOGkpsJWRkZHDw4EH++Mc/+jQGIdwheV70JWvWrGH48OEEBARQaislSAURZYjyWPuuwn3M6Bgptoguc6vYopT6Ac5VyxcCVyqlpBcheoWdO3eyZ88eLrnkEursdVQ5qnxWbAkxhBBmCKPMVsaA8QMwGAwyukX0WL7O87I2l/CU1atXExERQXN4M6EqlEBDoE/vn2xKBmBfyz4mT56MyWRi06ZNPo1BCHd0Ns8rpQYrpd5VSv0/pdQLSqmFrcdjlFKLlFIPKqVeVUol+iB8IdocO3aMI0eOMHr0aABO2E6QYkrxyLbPLiZlIsIQgUpRfPbZZ7S0SG1SdJ6704jGa61HAtu11h8CnvtJFsKLXnvtNYKCgpg6dSpFNueihb4qtgAkGhMps5cRFBbE5ZdfzrJly2R4ueipfJrnZW0u4Qk2m41PPvmEMWPGUO2o9ukUIpcQQwhppjT2t+wnJCSE8ePHs23bNiwWi89jEeIiOpvnY3DuWPSM1vo3wG1KqcnA/wCfaa2fAv4NPOvVqIU4x5lbPgMUWgtRKLIbsz16nxhjDM3RzVgsFjZs2ODRtkX/4G6xpab1v65/JRq9EMvXN5MnnsIDmpubeeedd7jpppsICQnhuPU4AOkB6T6LIcGUgEVbqHZUk5yczJEjR9i1a5fP7i9EJ/g0zwvhCRs3bqS6uprh44ZT66j16eK4ZxoTOIajlqM0OZqYOXMmDQ0NZGVl+SUWITrQqTyvtd7WWpRxMQANwPWAa/jWxtb3QvhMVlYWgwcPJjExkTp7HS26hUhjpMfvE2OMocHYgCHcIFOJRJd0Zjei/wMuVUo9C4R5MSZ54ik84le/+hVVVVUkJDi35jxuPU6cMY5QQ6jPYnBtC3rKdopJkyZhMBhYunSpz+4vRCf4NM8L4QkrV67EbDYTOsSZ1/1VbBkdOBoHDg5ZDjFq1CjCw8N58803/RKLEB3ocp5XSt0EfKK1PgQkAHWtH9UC0Uop07nXlJeXM2XKlLbXokWLPPAtiP6uubmZtWvXkp7ufHjqWhzXk+u1uLhGS6ZMSmkbTSNEZ7hbbFkA7ACKgMPAA16LSAgP+c9//kNMTAwjRoxAa80xyzEGBgz0aQxhhjBCVShltjLCwsIYNWpU2+5IQvQwkudFr7Ny5UrmzJnDCXUCgFhjrF/iGGweTJAKYn/LfoxGI9OmTWPlypVUVlb6JR4h2tGlPK+UmgPMAX7beqgMCG/9OgKo0lrbzr0uPj6enJycttf8+fO7G78QrF+/HovFwpgxYwDnei2AV0a2uIot9qF29u7dS0lJicfvIfo2d4stG4EcrfUvtdb/0FrLvxRFj1ZUVMTBgweZOXMmBoOBU/ZTVDuqGWEe4fNYEkwJlNnL0Fozffp0ioqKyM727JxSITxA8rzoVQ4fPkxubi5z586l0FpIqAolyBDkl1iMysjIwJHsa96H1pqZM2ditVplUXTR03Q6zyulrgeuBX4DJCmlZgKrgZmtp1za+l4In1izZg0mk4lhw4YBUGQtwqzMBKtgj98rUAUSpsKgdUdpmUokOsvdYkuu1nqH641Syj+PjoRw0xtvvNHW4QU42HIQgJGBI30eS6IpkRbdQqmtlAkTJhAWFsaSJUt8HocQF+HTPC9rc4nucv3s3HDDDRRYC4g1+bdrMjZwLFWOKopsRaSlpTF27FiZSiR6mk7l+dbFcJcBM4B1wIfAcOAh4Gql1MPAzcB9XotYiHOsXr2aoUOHEhjo3HmuwFpArDHWozsRnSnaGE2duY7k5GQptohOc7fYclQp9U2l1ECl1AC8PLxcOuGiOxwOB6+99hrDhg0jPj4egEMth4g1xhJnjPN5PAlG57otuZZczGYz8+bNY/ny5TQ1Nfk8FiE64NM8L2tzie565ZVXSEtLY/nHy6mwV/htChFAdmM2DY4GFIp/1/2b9U3rGTZsGJs3byY3N9dvcQlxjk7lea31dq11mNb68tbXVK3161rrSq31j7XWT2it79Jan/JR/KKfO3LkCIcPH2bcuHEAtDhaOGE74dX8H2OMod5RzxXfuoK1a9dis503Y06IdrlbbPkZzoT8OvAGMM9bAYF0wkX3rFu3jvz8fC677DIAHNrB/pb9RBmiWN+03uPbwl1MmCGMYBXMEcsRAO644w5qa2ulmCh6Gp/meSG6o6KigmPHjjFu3DgKrYWA/9ZrcQkyBJFgTKDIWgTAtGnTUErJSEbRk0ieF72aq+/sKrYU2Ypw4PBq/net21ITXENVVRXbtm3z2r1E39NhsUUp9ZxS6jrgD1rrOa4X8BPfhCdE5y1evJjo6GgmTpwIQKG1ECtWkkxJfolHKUWCKYFcSy5aay6//HJSU1OlAy56BMnzojdauXIlWmvGjRtHgbUAhfLbTkRnSg9Ip9ZRS429hqioKEaOHMmSJUtkUXThV5LnRV/x0UcfMXbsWOLinCPV8y35gHeL7a7fLcY0I0op2ZVIdMrFRra0AJ8D41sT9RAArfVnXo9MiC6oqKhg+fLlTJgwgYCAAAAOWpzrtSSaEv0WV5IpiVpHLSdtJzEajfzgBz8gKyuL0tJSv8UkRCvJ86LXeeedd4iLiyMjI4MCawFJpiQCVIC/wyI9wLkVqWt0y8yZMyksLJRF0YW/SZ4XvV5lZSUbNmzgxhtvbDuWb80n1hhLsMHzi+O6BBuCCVbBlKpSMjIypNgiOuVixZZmrXULzoWvDFrroz6ISYgue+utt7DZbMyaNavt2O7m3cQYY7yaiC/GVeg5ZDnEokWLCAkJwW63y+KJoieQPC96ldLSUj7//HOmTZsGOEcvDgwY6OeonEIMIcQZ49qKLRMmTCAoKIg//vGPfo5M9HOS50Wvl5WVhd1ubyu2OLSDXEsugwIGef3eccY4CqwFjB07lq1bt3LixAmv31P0DRcrtmgArbUdaBsDq5SSOZ6ix9Fas3jxYjIyMkhLSwOgwlZBgbWAAQED/BpbmAoj1hjLoZZDACQlJTFkyBBeffVVtNZ+jU30e37J87IQuuiqt99+G4fDwfTp06m0V1LrqCUjIMPfYbVJD0in0lFJua0cs9nM5MmT2b59O42Njf4OTfRf0p8Xvd6///1vIiIi2LHDuaFWia2EOkcdowNHe/3esaZYyuxlDJ84vC0WIdxxsWLLtUqpPyul/gxcdsbXv/FmUNIJF12xZcsW9u3bd9aolp3NOwEYYPJvsUUpxQjzCHItuTi0s58za9Ysjhw5wvr16/0am+j3/JLnZSF00RUOh4OXX36ZWbNmkZSURK7FudPPMPMwP0f2Ndcom61NWwGYMWMGLS0t0jkX/uSXPC+Ep9TV1bFq1SomTZqEweD85+uq+lUA1DpqvX5/126mjdGNDB8+nA8++MDr9xR9w8WKLRagofW16oyvrd4MSjrhoisWL15MaGgoU6dObTu2o3kHaaY0wo3hfozMaXjgcBp1I0U25/DySZMmERQUxAMPPMCiRYv8HJ3ox7qc55VSSUqpxUqpbWccC1JKvaiU+oNS6jWlVM/5V7Do9dasWUNeXh6//OUvAThsOUyYIYxkU7KfI/taqCGURGMim5s2o7VmyJAhxMbGyrRR4U9+6c/Lw1PhKR9++CHNzc1t00cBSq2lRBmifLJMQKwxFoWiwFbAvHnz+PLLL6moqPD6fUXvZ7rI5/drrc/b30opNdlL8QjRJX/961956623mDJlCkFBQQDU2GvIs+YxN6xnFO2q7dUAfFz/MaMDRzM7ZDZTp05l8+bN3HbbbX6OTvRj3cnzs4APgQlnHFsAHNda/1kpNRZ4FbjMI5GKfu+ll14iIiKCiooKjEYjhy2HGWYehkFd7NmRb2WaM9nctJl8az6DzIOYPn06a9as4cSJE6SkpPg7PNH/+KU/73p4KkR3LV26lAEDBpCZmQlAo6ORcns5w83DfXJ/kzKRZkojz5LHeMZjt9tZuXIlP/rRj3xyf9F7ddg7uVBibj2+vaPr5Gmn8LWcnBxaWlq49NJLAchuzGZp7VI0Ggc9Y8vNYEMwkYZITtpOth279NJLsVqtbNt2wb9qQnhdV/N86znLgbpzDl8PbGr9fC/O3S8iuhunEMeOHSMrK4vLLrsMk8nEaftpKu2VPWoKkcuAgAEEEMDmps2AcyqRw+HgnXfe8XNkoj/qTp4Xwt8qKyvJyspixIgRbVOIdjbvxIHDp2syDjYPJs+aR2p6KrGxsbz//vs+u7fovbz1KMj1tFOdccz1tPNJ4H9xPu0UwiM2bNhAcnIygwY5VyTXWnPMcoxYYyyRxkg/R/e1ZFMyZbYybNoGQEZGBqmpqWzcuNHPkQnhMQmcXYCpbT0mRLf87Gc/QynF7NmzAdrWa/HVk83OCFABTAiaQE5TDjZtIzExkRkzZvDGG2/IouhCCNEJy5cvx+FwnLVMwLambYQZnJtP+Mow8zBadAuFtkImT57MJ598QllZmc/uL3onrxRb5Gmn8KU9e/aQn5/PrFmzUMpZ36u0V1LjqGFwwGA/R3e2FFMKDhycsp0CnAvnXnrppRQUFLB3714/RyeER5QBZy6SFNF67Czl5eVMmTKl7SVDzUVHGhsb2bhxIxMnTiQqKgpwrtcSbgjvUeu1nGl68HQadAO7m3cD8MMf/pB9+/axe/duP0cmhBC9x6uvvkpKSgrp6ekA1NprOWQ5xMCAgW39fl8YHugs7OdacpkxYwZ2u52lS5f67P6id/LlJGe3n3ZKJ1x0xiuvvILJZGLmzJltx45Zj2HEyEDzQD9Gdr4EUwJGjJywnWg7Nn36dEwmE6++KoO9RJ+wGpgJ0Lpmy26t9XlbBcTHx5OTk9P2mj9/vq/jFL3Iu+++S2NjI5dffjkAXzV8xZ7mPUQbo1nftJ7sxmz/BngBowNHE2eM47OGzwBobm7GZDJx3333+TkyIYToHfbs2cPWrVvPeqC6vXk7Gk1GQIZPYwkzhJFqSuVwy2FSU1OZOHEiS5Ys8WkMovfxZbHFraedIJ1w4b6GhgbeeustJk+eTGhoKAAtjhYKrYWkBaRhVmY/R3g2ozKSaEo8q9gSFhbG+PHjWbJkCS0tLX6MTojOUUp9A7gDSFZKPayUCgZeAAYqpR4Gfg/c488YRe+nteaFF14gJSWFoUOHAlDnqKNJN5FoTPRzdO0zKANXhV5FnjWPY5ZjhIaGMm7cOLZu3YrV6tVNYIQQok9YvHgxZrOZ6dOntx3b1rSNFFMKUcYon8aS3ZhNmCGMXEsuNm3jjjvuICcnh4MHD/o0DtG7+LLY4tbTTiE6Y+nSpdTW1rbN4QdnMrRoS4+cxw/OqUT1jvq2qUQAs2bNorKykhUrVvgxMiE6R2v9ldb6Hq11qtb6Ca11U+vrF63vf6S1zvV3nKJ3W7t2LXv37uWqq65qe7JZZCsCIDmgZ04hcrkk+BJCVSif1n8KOBfKraur45NPPvFzZEII0bM1NUP11TYAACAASURBVDWxZMkSbr75ZsLCwgDnMgHHrMeYGjT1Ild7R5IpCTt2jlqOcvvtt2M0GnnjjTf8EovoHbxSbJGnncJXnnjiCZKTkxk82Lk2i1VbWduwlkRjIvGmeD9Hd2EpAc5tP/e2fL1Gy4gRIxg2bBgvvPCCv8ISQoge6S9/+QtJSUlMmzat7VihpZA4YxzhhvAOrvS/QEMg3wj9BrtbdnPKdooxY8YQGRnJSy+95O/QhBCiR3vvvfeorq7mxz/+cduxbU3OjbWmBvuv2GLAwL6WfXz00UeMGTOG1157TUami3Z5a4FcedopvG7r1q0UFBQwe/bstqed/2n8DzWOGsYEjfFzdO0LN4QTbYgmpymn7ZjBYODXv/41W7duZfPmzX6MTgjfqKmpYf78+axcudLfoYgebM+ePXz66af86le/IiAgAIAT1hNUOap8Pl+/s7Ibs8luzCZIBaFQvF79OkajkW984xusWbNGhp4LIUQ7tNb88Y9/JCUlhSNHjrQd+7zhc+KMcRy0+Cd/mpSJBNP/z959h0dZpQ8f/55p6aSREEIgCaE3KQFEESmCKKLYXV33Z2Gx7k/e3X1t+166dl3FVdeyYtlVdJW1CywqK2AAkYAICKETSqhJSG8zmTnvH5OJCSRhkkxN7s915ZrMM8+cuYdMbk7u55REttZsBeD8888nPz9fRqaLZvlyGpHbpBMu3PHoo48SERFRvzBuuaOcReWLyDBnBPQ8foA0Sxq5tlxO1P6ybJHdbicsLIwXXnjBj5EJ4RvR0dHMnz+fmTNn+jsUEcBuu+02LBYLYWFh9ceyq7JRKHqZe/kxMveFGcLoZ+lHri2X47XHmTBhAiEhITKSUQghmvHtt99y+PDhRtNH82rzKHGUkG5O92tsyaZkjtYepaC2gIEDB5KRkcFrr73m15hE4ArIYot0wsWZrF+/niVLlnDBBRfUd8I/LPmQSkclN0Tf4NOt4NoizZyGQtUPhwQIDQ1l/PjxfPzxx+Tl5fkxOiGE8L8dO3awbt06zjvvvPoF0LXWZFdnk2RKIswQdoYWAsfgkMEYMbKobBFRUVH8+te/5t1336WwsNDfoQnhNXLxVLTVvHnz6NKlS6Ppoz9U/YABA6lm/+402sPUA4AtNVswGAwMHz6c1atXs2XLFr/GJQJTQBZbhDiTRx55hLi4OCZNmgTAxqqNrK9ez4zIGfQw9/BzdGcWbginr6Uv66rWobWuPz5x4sT6nTeEEKIze+CBBwgJCeGiiy6qP7bPto9Ce2HATyE6VaghlP4h/VlfvZ48Wx733HMPVVVVvP766/4OTQivkYunoi22bdvGV199xcSJE+unj9q1neyqbJJNyYQYQvwaXxdjF7qbuvNT9U8AnHPOOZjNZhmZLpokxRYRdFyjWv7whz8QFhZGYW0hC0oW0Mvci+mR0/0dntvGhI3huP04e2x76o917dqVzMxMXnrpJebNm+fH6IQQwn8WLlzI559/zvTp04mK+mUR3G8rviVUhdLT3NOP0bXNwJCBhKkwviz7krVr1zJkyBDmzZtHaalszCiEEC633XYbZrOZ888/v/7YDusOSh2lpFv8O4XIZUToCHZbd1NmLyMyMpJzzjmH9957jyNHjvg7NBFgpNgigo5rVMvdd9+NXdt5s/hNrNrK8JDhrKlaQ1Zllr9DdMuY0DFEGaJYVNZ4eO2MGTOw2Wx88803fopMCCH85/Dhw9xxxx2MGTOGadOm1R8/ajvKxuqNTAyfiFmZ/Rhh24SoEKZGTGVzzWZyrbnMnDmTkydP8tJLL/k7NCGECAhHjx5l3bp1jBs3rn67Z4B1VesIV+H1U3j8bUToCDSaTTWbAJg6dSp2u13yuTiNFFtEUHGNapkwYQIffvgh/634L/ts+xgbNpYoY2BvAXqqEEMIF0VexE7rTnbU7Kg/npSUxOjRo1m5ciX5+fl+jFAI75G5/KIpDoeDm2++mZqaGhYsWIDRaKx/bGnFUszKzAURF/gxwvaZEjGFSEMkX5R9QVpaGjNnzmTevHkUFxf7OzQhhPC7efPmYbfbGxXaS+2lbKzaSGZYJkZlbOHZvtPT1JNuxm58X/k9AAkJCVx55ZX8/e9/l9GKopGALLZIJ1w055FHHiEiIoJJkyZRWFvI4vLFnBVyFmmWNH+H1iYTwicQY4jhs7LPsGt7/XHX6JZnn33Wj9EJ4T0yl1805dVXX2XZsmXMmjWLlStX1h/Pr80nuyqbCeETgq6w3lCoIZTpEdPZbt3OzpqdDBs2jOLiYm644QZ/hyaEEH5VWFjI3//+d0aPHk1CQkL98RWVK6illikRU/wYXWOrqlbRw9yDfbZ9HLYdBqBPnz6UlJRw6623+jk6EUgCstginXDRFNeolqlTp5LtyObVolexa3vAzN9srazKLNZWrWVQyCD22/bzWdln9Y8lJSUxduxYXnrpJfbv3++/IIUQwkd27NjBvffey/Tp05kwYUL98azKLOYXz0eh6GLoEjRTRZtzfsT59UX2nj17MmrUKJYtW0Zubq6/QxNCCL958cUXqaioaLQoeqWjkpUVKxkeOpwkU5Ifoztdb3NvDBhYWbkSgLS0NPr168e3336L1Wr1b3AiYARksUWIptxyyy31o1qO1x4nrzaPoSFDiTBE+Du0dkm3pNPP0o9lFctYW7m2/visWbMwGo3ce++9foxOCCG8z2azMX36dAwGA5MnT0YpVf/YIdshDtoOMiRkSFBt99yUrMosfqj6gf4h/cm15bKgZAE9Z/TEYDDwu9/9rtHudEII0VmUlpYyb948hg8fTnJycv3xT0o/oUpXMSNyhh+ja1qIIYTelt6srlzNIdshAKZPn05RURFvv/22n6MTgUKKLSIorFq1iq1bt3LhhRcSEhLCpupNhKkw+of093doHjEqdBT9Lf15p+QdVlasBCA2NpYpU6bw0UcfkZUV3FdyhRCiJY8//jgHDhzghhtuIDo6uv54uaOc7KpsYg2xDA4Z7McIPau3uTexhlh+qv6JkJgQLrnkEpYsWcKXX37p79CEEMLnXn31VSorK7n44ovrj+XU5LC6ajUDLAPIteUG5KjG4SHDiTBE8E7xO1Q6Khk0aBAZGRk89thjVFVV+Ts8EQCk2CICntaaBx54gOjoaCZNmsTmms0U2AsYFjoMkzL5OzyPMCgDd8fdzdCQoXxQ+gELSxZSq2uZNm0asbGx3HPPPdhsNn+HKYQQHpednc0TTzzB2WefzahRo+qP27WdBcULqNE1jAsfh0F1nC6LQRkYFTaKSl3J9prtTJkyhcGDB3PXXXdRUFDg7/CEEMJnioqKePbZZxk8eDCpqakAnLSf5K3it0g2JTMsdJifI2xeiCGEG6Nv5EjtEf5S+BcK7YXMmjWLI0eO8Oqrr/o7PBEAOk7PRXRYS5cuZc2aNcyYMQOj2chnpZ8RZYiit7m3v0PzKIuycHvs7UwOn8zyyuU8V/gc5cZyrrnmGjZt2sRTTz3l7xCF8BhZCF0AVFZWcumll9KlSxeuu+66+uMO7eCfxf9kU80mRoaOJNYY68covaObqRs9TT3ZVrONQ/ZDzJo1i+PHj3PDDTdIcV0I0Wk88cQTFBUVcfnllwNQq2uZVziPakc1o0JHBfyF1bNCz+KeuHsothfzWMFjbO++naR+STz88MMUFhb6OzzhZwFZbJFOuHCxWq3cd9999O7dm3PPPZeVlSs5Zj/GyNCRHeoqJzjn8q+pWkN3c3fGh4/naO1RHs9/HMsgC9dffz2PPvooK1as8HeYQniELIQuAO69916OHz/OTTfdRFiYcz2Wakc1bxe/TXZ1NpdHXc6AkAF+jtJ7xoSNIVSF8krRK0T2iORXv/oV33zzDf/zP/+D3W4/cwNCBDDpz4sz2bdvHy+++CLjxo2jZ8+eAHxc+jEF9gLGhY+ji7GLnyN0T/+Q/twXfx8KxcqKlQyeNZiqqiruu+8+f4cm/Cwg/1qVTrhwefLJJ9m6dSvPP/881YZqFpctZpBlED1MPfwdmlelmlN5sOuDxBhj+FvR3zBebiRjcAZXXXUVe/fu9Xd4QgjRbv/5z3945ZVXmDJlCgMGOAsq22u282jBo2yo3sDlUZczPXK6n6P0rlBDKBMjJmLVVl45+QpDxw3lmWee4YMPPuC2227D4XD4O0Qh2kz686IlWmvmzp2LwWDgsssuI6syi3eL32VF5QoGWAbQy9zL3yG6Jasyi6zKLHbbdjMxYiI1uoYtUVuYfMFk3nrrLVatWuXvEIUfBWSxRQiAr7/+mscee4wxY8Zw9NhR3il+h2pdzdVdrm60U0VHtdO6k/PCz2NQyCDe2/YeeVfkUT6onBmXzaCoqMjf4QkhRJvt2LGD66+/nrPOOotZs2ZR7ajmXyX/4oWTL2DVVqZGTCXcEB6QCyJ6WowxhnFh4zhae5SH8h/ia8PXDJk2hLfeeoubbrqJ2tpaf4cohBAe989//pNFixZx6aWXEhMTQ4m9hHVV60gwJjAidIS/w2uTOGMcY8PGkm/PR01WpKamcvPNN3Py5El/hyb8RIotIiCtXbuWa665huTkZG644QZeLnqZLTVbGBk6kj22Pf4Oz2eMysiI0BFMj5xOT0tPrBdY2XnRTgbdMYjtB7f7OzwhhGi17du3M3nyZEJCQvjiiy84zGEeK3iMrMosBlgGcHHkxSSYEvwdpk8lm5OZHjmdEBXC8orl1Jxfw8WXX8yCBQu4+uqrqa6u9neIQgjhMevXr+e2226jf//+TJkyhQpHBVmVWZiUifHh44N6qYB0Szp9zH1YVr2M0b8azf79+7n22mulcN5JBe8nWXRYixcvZtq0aSQmJnLTXTfxQfUHbKvZRoY5g36Wfv4Ozy/ijHGMCh3FlIgpxJniODbwGIPfGMwNH95ATn6Ov8MTQogz0lrz/vvvM3bsWMrLy7n9jtt54NMHeLbwWTSaP8b/kVFhgb8YorfEGGOYHjmd/pb+7Lbt5vsR33Pub8/l888/55JLLqG8vNzfIQohRLtt3bqVSdMmYeppYtLNk1hVtYonCp6g3FHOueHnEm4I93eI7TYqbBQ9TD1YGraUS26+hP/+97/Mnj1bCi6dkNJa+zuG0/Tt21dPmjSJmTNnyjzPTkJrzc/Hf+bhtx7m8x8+J6xrGNHp0RzXx1EoBoYMZGjIUIzK6O9QA8K+/fvIPpiNfbAdTJCZnMkX131BclSyv0MTraCU+lFrnenvOPxB8nznkpOTw9y5c1m2bBm9e/dm1q2z+EB/wNHaoySbkjkn7BxCDCH+DjNgFNQWkFOTw6HaQ/Ss7EneW3mM7TuWRYsW0bVrV3+HJ1qhM+f5zMxMvWHDBn+HIQLIylUrufj/XUx1ZjW6yy9/gyYYExgZOpKupo6T30rtpSyrWAbA6G2jWfXvVVxyySUsXLiQ8PDgLyiJX7SU5wOy2CLJufM4WXWS97a8x1Mrn+JY9TEAjDYjiaGJxJviSTWnkhma2ammDrmr5FgJKz9YSXlGOeo8RbglnCcveJI7R9+JydA5rwwHG+mES57viE5UnCAnP4fdhbs5UXKCt999m9x9uZgw0fesvhiSDWy3bseAgWGhw+hv6d8p1uFqLYd2sNO6k83Vm7FrO+yB0J2hPPbbx7j7N3cTGhrq7xCFGyTPS54XUF1bzd2v383be95Gx2jidTwZ4RmMDxtPnDGOrsaurKrqeAvJlthLWF6xHJu2Mez4MH58/Uf69+3PM888w8yZMzEYZJJJRyDFFuE3NruNw2WHKa4upqS6hJKaEoqrizlSdoTle5ez8sBKbNoGR8CwxcDg1MEMGTNEko+baq21bF22ld05u7FOtkIfSDGl8OjkR7np7JvkD5gAJ51wyfMdxf7i/SzYvIA3f3qTgyUHmz3PgIEkUxL9LP2IM8YRZgjzYZTBqcpRxW7rbnZV7aLGUAOAKlMkkMCw7sOYMnQKEwdNZETSCEJMMjoo0EielzzfGdgddnYU7KDCVoHVbq3/OlZ+jC9yvmDJziXYDDYiyyK5NuFaqixVnaaPWu2oZnvNdnKsOcTaY6nZUENlbiW9wntx4yU3cs2V1zB06NBO8+/REQVUsUUpdQFwBXAC0FrrR049J5iT8/z585kzZ46/w/CJpt5rQWUBK/evZOX+law+uJqc/BxsDlvTDeQDeyF0RyhpqWkMmjSIsOjA7Hjv/n43fc/p6+8wmlVTWUPNuhqyy7I5PvQ4xEFIZQijokZx2ajLmDZ8GgMSBhBqOvOV0M7+GfaljtoJ7+h5Hvz/2fGVlt7nyaqTfL3na97/+X3+s/s/aDQ9QnsQeSySgysOUnWgivS+6fQ9vy+xybHYsWPCFLDTQQM9zzu0g5P2k+w7uo9j5ccoN5aj4zSYnY8bHUb6hfdjWv9pzBw2k3N6nkOYuen/UzvL5xf8/147ap6HM+d6yfPBoa3vs6S6hG/2fsPi3Yv5dPunlFubXldKVSj0Tk2qOZVxZ4/DaPLf/wH+yvNaaw7aDrLTupN8e/4vD9iBkxBRFcHQ7kOZPmo6F4+5mPTYdOLC4tq1UHBn+fyC/99rwBRblFLhwBZgsNa6Rin1CfCq1vrbhuf17dtX79692+12Fy1a1Ko5/948PzMzk9b8x+LL2G12G2XWMkqqSyitKaXGXoPJYMJsMGNQBhZ/u5j+I/qTX5FPQWUBBZUFGJSB6NBouoR0oUtIF6JDnN9Hh0Yz6/JZvPPBOxwoOcCW41tYsX8FW45vAcCszCSqRMLKwrAfs3Ny/0lK8kqgGqiBLlFd6NG7B8kDk0lIT2gy8eZtzSNlSIpb77M157bl/KXzlnLRHy7ySvuejF1rTdGxIrYe2coxyzFs3W31HXE0xJviSYxMJDk6me7R3UmMSCQhIoGu4V3pEtIFgPsevI8HHnqASlsltY5aEiMSSYpMontkd5Iik4gPj2+U/E/9TNoddkpqSjhZdZKiqiKKqovQWmMxWsj+IZupE6cSFxZHfFg8kZbIFiv53v796NOnD3v2ND9FzaEdKBRKKa/E0hE74e7m+czMTP3www+7/W/qqX9/h3YAnNaBaW37Z8r1rv9bvfXZsTvsGJShze3PuGQGFdYKyq3llFvLsdqtWIwWzEaz89Zgxmw0M3LESL5a/RUHSw5yoPgAu0/sJud4DjsKd7CnbA8ajaXWQvSBaCqzKqk4UAEKegzqwYDzB9CtT7cWf8e9mbsDKc+39nx3ztVaU3K8hIMHDrL/8H4qulTg6OGAJMAAyq6IKY8h1ZDKgJgBDO0xlL69+pKUmMSt/3srz772LCcqTnCs/BjHK45zvOI4NrsNgzIQZg4jJiSGmNAYokOjObDrABNGT6i/HxNa91hINKGm0EY/40Dqk2mtyRiQwfqf1lNhq0ChCDOHEW4OJ9QU2u480FnzPLiX6/2V5z11fmv69P6M3Wq3UlhZSEFlAYVVhTi0g3Vr1zFpwiTCzeGEm8Ox2W1U2ipP+1JK8ft7fs/rr76OxWjBZDBRWFXI4dLDHCk7QkFVAWaDmVBTaP1Xzs4cisKLyDqQhUM7CDOGkWpKJbY8lqqCKkqPl1JyrISTR06iqzRRKorxN44nLiXOrffa0fO8TdsotZdS4ijhYN5BKs2VWE1WKkIqoOGfQxpizDEkhCeQFJWEo8zBqAGj6BbZjcSIxPqvbhHO+xGWCBzaQZWtikpbJZOnT+bfn/67/mcdagolJjSmvu/dsAZgNppZ/d/VzLp0ltvvtTWfYX/359vTfnvzvK8XdhgHHNBa19TdXwPMABp1wouLi1vVaKAl59bwdLI9/5/nU+uorf86eOQgYTvDKK0ppaq26swvsOuXb1Wts/OkTc0U5C6GCxZc4Py+FgyHDbAXyAXbYRuHHYcBCI8JJ65nHEabkbNmnkVczzhCI888wuLwtsNuJ7jWnNuW81vLX7ErpYjrHseE7hMAKCsuY9WXqzB0M1BkK6IwqpDCiEK2h2+HcJxfllMaGQm3Lb6t2ddXDoWpxoTJZkKhsFqthH8fTlxSHEVVRZTUlLQY//277q//3mwwExcWR0xoDGajs+jX8OvA/gNknMhAa41GN7oFTjt2KO8QPfJ6NDoONPl8jSZ3Zi4ZL2U0aq/WUUulrZIKawU19hoUihBTCA6rg+gd0YSYQggxhjS6/fzaz+kW2a3R+/Rm3ghwbuV58Px/du9teY+Xs1+m1lFL7oFcuh/qjs1hq+94VNoq6/OgQRnqi81mo5nqimpid8ViNprrj5kNZuc5RmfF0u6wY9d27A47uyfuZsDLA+rv27WdmtoaqmurqbE7b1v67FiMFixG5y+fQzvQWuPQDhzawaG8QyTnJdcfa/i5dH1Z7VYA5/pMtRCxLeK0Qonre6PBWP9vUGGroLi8mNqNbu6IcB30f7n/L/c1UIJzZOIRYBfYjtmwdbORmJxIhamC8f8znrAo90YpejN3B1Keb+357pyrlCImKYaYpBiqFlYxevJoio8Uc2zHMQocBRSHFFMaW8qmuE1ssm/iw4MfgmuG10SYtfCXTrWqVqgqhXIolEGhTRpt0TjMjvp9K189+GqTcViMFqJDorEYLWg0xcXFdNnVpf5z3TDnhhhDGv3RZjKY2L9/P2nH0k57b02+ZxS5ubmkH0+vP+bQDmwOGza7rX7qQlVtFRXWCiptlejrNV2fbXrRzTCTs/ASYYkg3BxOwZEC0o+lY1AGbhh6A78b+7sWfwadOM+Dm7k+kP4gC5b+fHPnb+qyiUW7FjX6f6a4upjSmtKmn+TudevzG+cDF2VTqBoFCjCCNmrn3wQOMBYacex2wA6oyqtih94BgMFkIKprFFFdoxjcfzDJg5LZ+8Netwst0PHzvFmZiTfFE088+evzmXjtRACqKqrYu2cvx04eo7SqlCp7FcXhxRRHFLM7YjdEwJoTa6CZP6GUQ6ENDf5muwAGvTrI7djRELs9lvjweCLMEfX9n8W/Wkx8ePxppwfS73ZJSct/e7Sn/fbmDV+PbPkVcK3Welbd/dnARK31r085rwpwNDiUDxS00HQ0zu6fu7x5fldajtWXsbT2/Na23Ytfum7eaN+bsbf2fG/+XAPpMwCd67225jPsjVhStdYJrWgz4LUiz5fhHHdlrzsUTHkeWvd7Emixt+b8QMrzrT0/kHJfa88PpM9va9sPpM8ASJ73CndyveT5oDlf8rzn2pf36vu2wbuf4XbleV+PbDkBRDW436XuWCNa68BcuEMIIcSZuJvno049JoQQImicMddLnhdCdHa+3vJlLZCqlHItl38usMTHMQghhPAeyfNCCNHxSa4XQogz8MduRFOBq3AOJbQ1tUuFEEKI4CV5XgghOj7J9UII0TKfF1uEEEIIIYQQQgghOjJfTyMSQgghhBBCCCGE6NCk2CKEEEIIIYQQQgjhQVJsEUIIIYQQQgghhPAgKbYIIYQQQgghhBBCeJAUW4QQQgghhBBCCCE8SIotQgghhBBCCCGEEB4kxRYhhBBCCCGEEEIID5JiixBCCCGEEEIIIYQHSbFFCCGEEEIIIYQQwoOk2CKEEEIIIYQQQgjhQVJsEUIIIYQQQgghhPAgKbYIIYQQQgghhBBCeJAUW4QQQgghhBBCCCE8SIotQgghhBBCCCGEEB4kxRYhhBBCCCGEEEIID5JiixBCCCGEEEIIIYQHSbFFCCGEEEIIIYQQwoOk2CKEEKLNlFJhSqktSqnn6u6HKqVeVko9oJR6WynVz98xCiGEEEII4WsmfwcghBAiqD0O/NTg/lzgoNb6L0qpocBbwHl+iUwIIYQQQgg/CchiS9euXXVaWpq/wxBCCK/68ccfC7TWCf6Oo62UUjcCa4BhQGTd4RnAgwBa65+VUmcppbporUsbPlfyvAgY+fmnH0sI2l9LEWCCPc+3h+R5EVAa5nrJ8cKDWsrzAVlsSUtLY8OGDf4OQwghvEopdcDfMbSVUmoQMFBr/aBSaliDhxKBsgb3S+uONSq2RERENGpvzpw5zJkzx0vRCtGC+fNPPyafReEhwZzn20v68yKgNMz1kuOFB7WU571abFFKhQHrgG+01n9USoUCzwGHgb7A01rrXd6MQQghhFdcDlQrpe4HxgMWpdRc4AQQ1eC8LnXHGklISJBOuBBCCCGE6LC8PbJF5vKL4HXq1U6pggtRT2v9hOv7ukJ6pNb6hbrvxwGr6vL85lOnEAkhhBBCCNHReW03ogZz+XMbHJ4BrAXnXH7gLKVUF2/FIES72e1QWenvKIQIWEqpK4EJwNlKqV8BLwKpSqn/B/wBuNWf8QkhhBBCUFMDP/8MWvs7EtGJeGVkS3vn8ufn55OZmVl/X+byC795/31YuxaGD4eJE6Ff63extdls5OXlUV1d7fn4RFAIDQ0lJSUFs9ns71A8Tmv9CfDJKYfv8kcsQrTJkSPwyScwYQIMGwZKtboJyfOiI+d5ITqExYvhm2/aPFJd8rxoS5731jQimcsvgt/Bg/D999C7N+TkwPTpcP/9vzzuZrLOy8sjKiqKtLQ0VBs68SK4aa0pLCwkLy+P9PR0f4cjhDjVd9/B1q3Orz594I47Wt2E5PnOTfK8EAGurAxWrXJ+/+9/w3PPQVRUy885heT5zq2ted4r04i01k9orR/VWj8NrAaytdYvAEtwzuVH5vKLgKY1fPwxhIfD3XfDJZdAbi4cPtzqpqqrq4mPj5fE3EkppYiPj5crIacoKSlhzpw5LFq0yN+hiM7M4YCffnKOaLnuOtizB7KzW92M5PnOTfK8EAHu7behqgquugqKi+Gjj1rdhOT5zq2ted5ra7aAzOUXQWzNGti501lkCQ+HcePAZIKsrDY1J4m5c5Of/+mio6OZP38+M2fO9HcoojP74QcoKYHMTJg0CeLjYVfbNkmU3/POTX7+QgSwr7+G5GSYMgVCQ2H9+jY1I7/nnVtbfv5e3Y1I5vKLoPXVV2AwfAiTRgAAIABJREFUwDnnOO9HRsLIkc6O+RVXQEiIf+MTQgjRfp9+CkYjDB3qvN+v3y8LKEqnWoh2cY1gnDlzphTWhX9t2QK9ejn79qmpIMtVCB/x6sgWIYLWihXOZBwa+suxCROguho2b/ZfXEIIITxDa+fCuAMHOkcwAvTtC+XlznW6hBDtIiMYRUAoLHQuA9Cjh/N+aqqzL19T49+4RKfg1ZEtbSWVcOFX5eXOOfsXXND4eEYGhIU5pxeNGdPm5ufPn9/OABvzxE5dOTk5XHPNNdxwww189dVXLF++HKPRyFdffcU999yD3W5n9uzZ3N9wgeAGmjuvtcerq6uZMGECNTU11NbWctVVV/HII480G3d747vllltYvHgxiYmJbN261a0Y0tLSiIqKwmg0YjKZ2LBhA1arlQsuuIDly5djMgVkWhVCnGrTJti/H2688Zdjrh3nvvsOBg9uc9OS51vOz8XFxcyePZutW7eilOLtt99m3LhxrXpdd89rKs+72O12MjMz6dGjB4sXL64/3tRzJM8LEaR+/tl5m5LivE1LA5vNuSj6qFFtblbyfODn+eaON9fX90aeD8iRLVIJF361Zg3U1kL//o2PGwzOnSp27/ZPXB6ybds2tmzZ0uhYVlYWixYtwuFwcMUVV2A0GrHb7dx1110sXbqUnJwcPvjgA3KauNrb3HmtPQ4QEhLC8uXL2bx5M5s2beKrr77ihx9+aPJ9tDc+gJtuuomvvvqq0fnuxLBixQo2bdpUv2uaxWJhypQpLFy40M2fgpAFcoXfffGFM6+fddYvx7p2hdhYZ7EliAVynge45557mD59Ojt27GDz5s0MHDiwyffhrTzv8uKLLzb52k09R/K8EEHKlQsbjmyBNq/bEigkz585zzd3vLm+vjfyfEAWW4TwqxUrwGx2FlZO1a8fHD/uXFAxSH333Xe89tpr9ferq6uJjY0lPT2dJUuWcNlllwGQnZ1Nnz596N27NxaLheuuu44vvvjitPaaO6+1x8G58FRkZCQANpsNm83W7GJU7Y0PYMKECcTFxTU6vzUxNDRr1izef//9M54nnKSoLvxu1SpnoaXh9p9KOacSffedc5pRkArkPF9SUkJWVha33urcI8FisRATE9Pk+/BWngfnNq5Llixh9uzZpz3W3HMkzwsRhLZsgYQE6NLFeT8+HuLinDvRBTHJ82fO880db6mv7+k8L8UWIU61YgWMHQsWy+mPuYaYt3G3ikBw4sQJPvvsM8rKygBYuXIlF1xwAVarlX379pGWlgbA4cOH6dmzZ/3zUlJSONzE1tfNndfa4y52u53hw4eTmJjI1KlTGTt2bJPvo73xtaSlGJRSTJs2jVGjRjUaQjpkyBDWB/lVEiE6DbvdOV20qSHNrqK65Pl6nszzubm5JCQkcPPNNzNixAhmz55NRUVFk+/Dm3l+7ty5/OUvf8FgcL8rLHleiCC0ZYuzsO66cKYUDBjgXBYgiEmeP3Oeb0lzfX1P53kptgjRUFUV/PgjnH9+04/37OlcNDeIpxJ16dKFq6++mn/84x+Ac05lfHw8BQUFzVadfcloNLJp0yby8vLIzs4+bY69v2NYvXo1GzduZOnSpbzyyitk1W0HbjQasVgs9f/pCSEC2LZtzvW5miu2QFBPJQrkPF9bW8vGjRu54447+Omnn4iIiODpp5/2aQyuOfyjWrleg+R5IYLM/PnOfF9b2/j4gAGwY4d/YvIQyfPt01xf39N5PiCLLTKXX/hNTo7ziueIEU0/bjQ6F8oN0iueOTk5DBw4kHvvvZe//e1vlJeXY6kbwRMWFkZ1dXX9uT169ODQoUP19/Py8ujhmu/aQHPntfb4qWJiYpg0aVKzc+3bG587morB9dzExEQuv/xysrOz6x+rqakhtOEOVkKIwLR2rfO2qWJLYqJzmHmD3+1gEuh5PiUlhZSUlPqriFdddRUbN25s8r14K8+vWbOGL7/8krS0NK677jqWL1/Or3/962bPb0jyvBBBpLLS+dW1a+PjxcXOEYwvvOCfuNpJ8nzr+vMtaaqv78k8H5DFFpnLL/zGtdDUsGHNn9O3Lxw96txKLkj8/PPP7N+/n48//php06bRs2dP5syZw+WXX87ZZ58NQGxsLHa7vT5Bjx49mt27d5Obm4vVauXDDz/k0ksvPa3t5s5r7XGA/Px8iouLAaiqqmLZsmUMGDAAgClTpjQaLtje+JrTUgwVFRX1le6Kigq++eYbhgwZAkBhYSFdu3bFbDa34icjhPCLtWudc/h79z79MaVg5Migm88fLHk+KSmJnj17srNuCP+3337LoEGDAN/l+aeeeoq8vDz279/Phx9+yOTJk3nvvffO+G8seV6IIOPqq8fHNz7erZvz9vhx38bTTpLn3c/zLWmpr+/xPK+1DrivUaNGaSH8Yu5crcPDta6t1fr115v+mjtXa9D666/dajInJ8fLQZ/ZSy+9pFNTU/WKFSvqj9ntdn3fffc1Ou+WW27Ry5Ytq7+/ZMkS3bdvX927d2/9+OOP1x+/6KKL9OHDh894XmuPb968WQ8fPlwPHTpUDx48WD/yyCP1sfbq1UtXVlY2ire98V133XU6KSlJm0wm3aNHD/3mm282G4PWWu/du1cPGzZMDxs2TA8aNKhRWx999JH+/e9/39Q/v9a66c8BsEEHQM71x1efPn30b3/7W/3ll182+28mhNf066f1pZc6v28qz993n9Zms9Y1NW41J3ne/eNaa/3TTz/pUaNG6aFDh+rLLrtMnzx50qd5vqEVK1boGTNmNDrW3HMkz0t/XgSZ22939tn/9KfGOf6RR5zHb7rJ7aYkzwdfnm/ueEt9fU/neeV8PLBkZmZq15aqQvjM/Pnw179CdTU88EDz51VUwO9/D08+2fJ5dbZv397sdmeBZuPGjfz1r39lwYIF/g6lka1bt/L222/z/PPP+zuUZl1xxRU8/fTT9HOt93CKpj4HSqkftdaZvogv0EieF35TWOgcUv7UU3D//c7cf6qYGLj2Wti4sflppQ1Inm8/yfMdj+R54XfXXAMffQTPPw8REb8ct9vh7rvhwgvhP/9xqynJ8+3XGfN8QE4jEsIvtIa8PEhJafm8iAjn8PMff/RNXD40cuRIJk2ahN1u93cojQwZMiSgE7PVamXWrFnNJmYhRAD54QfnbVPrtbi4CizNzDEPZpLn20byfOvJGozC7woKnBtbhIc3Pm40Ovvyx475Jy4vkzzfNt7I8yaPtSREsCstde5O4c5iS716QQe9WnPLLbf4O4SgY7FY+M1vfuPvMIQQ7li71tnRzmxhsEFGBkRFBd26Le6SPN96kudbz7UGoxB+4xrJ6Nr2uaGkpKBbs6U1JM+3njfyfECObJFKuPAL12JN7hRbUlPhwIGgWiRXCCEEzpEtw4Y1HlJ+KoPBObqlA45sEUKITqOg4PTFcV26dYMTJ5xTioTwkoAstshuRMIv8vKct+6ObIEOOZVICCE6LLvduaVz3a4NLRo5EjZtko64EEIEI62dF0WbK7YkJUFtLezf79OwROcSkMUWIfzi8GHnooiRkWc+11Vs6aBTiYTwNhnBKPxixw4oK3Ov2DJiBFRVQd3WlUIIIYLIyZNQU9PyyBaQHC+8StZsEcLl6FHo3t29cyMinHP6ZWSLEG0ic/mFXzzzjPN2//6mdyFqaORI5+1PP8GgQV4NSwghhIcdOuS8jYtr+vGkJOftjh1w8cW+iUl0OjKyRQhwDjU8fvyXKrc7XEPMhRBCBIfcXOeuFImJZz53wADnLhaybosQQgQf1/IAsbFNPx4Z6bx4KiNbhBfJyBYhwLlAVnV164otw4bBRx85h6RHRbn/PE9fzZ8zx63Tjh49ynPPPUdMTAx2u52dO3eSlpbG5Zdfzr333ovVamXatGkAnDx5kt69ezN37lzPxio6DKWUAVgErAMsQAZwCxAGPA3sA/oCD2qtO+5y/yK45OZCWppzAdwzMZlg+HBYv771r+OHPJ+VlcVDDz1Ebm4uu3fvxmKx1D923333sWDBAh599FH69+/PG2+8QUZGBkVFRezevZsvvviCffv2ce+997Jr1y6uueaa+ufu2LGDqqoq+vbti81m46WXXuIPf/gDQP1zhRAi4LiKLTExzZ+TlNT2YkuA5vnbb7+dffv2sWDBAn7++WcGNRiZuXTpUi6++GIeeOABHn30UZ588kkefvhhXnvtNW6//fb68woKCkhPT2fmzJk8+uij9OnTx7PvtRMJyGKLay7/zJkzZZFc4RuuRNvaYgvAtm3uzf/3o+rqai699FI+/fRTevbsCTj3kr/++usZM2YMEydOpLy8nD//+c8AFBYWsmPHDj9GLILEWq314wBKqS+AK4DzgP9qrf+tlJoJPAfc6McYhXAqK4MjR5xrsZyJqxMdHg6rV8Nrr8Edd3g3vnaaMGECEydOpLKykjfffJM777wTgBMnTpCdnU1ycjKzZ88mMzOTt99+m2F1/4fdeeedOBwO+vXrx6xZs1i8eHH9/wUAH330EeXl5dx8882Ul5fz0ksv8dxzzwHwj3/8w+fvU/ieUioMZ2H9G631H5VSoThz+2GcRfWntda7/BmjEKc5fNi55XOXLs2f062bcxpRkHAnzz/00EOsXLmSnJwc/vznP/Pvf/+7/vlLliwB4MEHH8RkMvHQQw/x5Zdf8tRTT3HzzTcTEhICwCuvvEKvXr24/vrrpdDSTgE5jUh2IxI+t6uuj9CWYsuWLZ6Px8OWLFlCWlpafaEFnHvJf/zxx6ede+zYMf72t79x7rnn+jJEEWS01o4GhRYTkALsBGYAa+tOW1N3Xwj/27DBOWU0Pd3956Sng9XqLNIEiYceeoinn36ampoaAF599dX6DjlAUlISzz//PHv27Kl/vOHV0Ybuvvturr76am6++eYmH2/uuOhwHgd+anB/LnBQa/0U8FfgLb9EJURL8vIgOhqMxubPSUpyLiNQUuK7uDzgTHke4NZbb2XdunX8/PPPACxevJgZM07vkg0ZMoSxY8fy5ptvAs7CjVKKhIQEL7+LzsErxRallEEptUQp9ZBS6nGl1AdKqTClVJxSar5S6n6l1FtKqVb8ZSuEF+3a5Rwy3twiWk1JTXVOHwqCYsu+fftIci0EBhw4cICnn36amTNnsr9uy7sVK1Ywd+5cHnroIT9FKYKRUupCYDGwWGu9AUgEyuoeLgVi64oxjeTn55OZmVn/JYvlCq9bt855m5bm/nNchZncXI+H4y1Dhgxh3LhxzJ8/n+PHj2MwGBp1mt99910SEhKYOnUq/fv358UXX2z0/M2bNzN37lzmzp1LSZD9ASI8Tyl1I87CecNfgvqiutb6Z+AspVQLwweE8IO8vObXa3EJ0h2JzpTnAUJCQrj//vvrRyouXbqUiy66qMn2Hn74YZ555hmqq6t55ZVXTivciLbz5jQiGV4ugsfOnc4FE92Zx++iFAwdGhTFloyMDNa5/tAAUlNTuf/++0lLS6O8vByASZMm8dxzz2G1WslzzXMV4gy01l8DXyul3lVK3QmcAKKAYqALUKS1rj31eQkJCWyQrdOFL/3wgzPPR0a6/5yuXZ3n79vnvbi84OGHH+bCCy/k4MGD3HfffWzdurX+sbi4OJ599lmeffZZNmzYwJVXXklGRgaXXHIJAGeddRYvvPACADuD7A8Q4VlKqUHAQK31g0qpYQ0ealhUB2dhPbHutp6rqO4yZ84c5ri5zpwQ7eZOsaXhjkRjxng/Jg9qKc+73HrrrTzzzDM88sgjXHrppc22NXjwYM455xweeeQRwsLC6Nq1qzdD71S8MrJFhpeLoLNrl3u7UzQ0f75zNMyGDfD6696Jy0NmzJhBbm5u/SgWAK01drv9tHMtFgtJSUksX77chxGKYKOUGqSUapjDc4HewBJgXN2xc+vuC+FfWjuLLb17t+55SjlHtzTIncFg0KBBTJgwAYvFclqneerUqWitAcjMzGTYsGFYrdYm2+nfv7/XYxUB7XKgWil1PzAeGKOUmssvRXWXLnXHGnEV1V1fUmgRPpWX1/LiuAAJCc6+fBAWllvK8y4Wi4UHH3yQZcuWceGFF7bY3sMPP8z8+fO56667vBFup+XVBXLrhpf/H+qGlyulmhxefupVT6mEC5+qrYW9e2Hy5NY/NyUFsrKgqMjzcXlQSEgIixcv5plnniE2Nha73c6ePXu46aabKCwsJCsrC6vVyuOPPw5AZWUlFouFyW35NxGdRQ1wq1JqBGAGBgL/C1iBZ5RS/XDuUPRH/4UoRJ2DB53z8idNav1z09Ph55+huPjMHXc/2rBhA1lZWZSXl/PUU0/x/vvvA84+1YIFCzh69Cjvv/8+AwcO5JZbbiE1NZWTJ0+Snp7OZZddxp49e1i0aBE7d+7kzTffZPbs2ae9hmvEy/PPP8/vf/97n74/4Xta6ydc39ctihuptX6h7vtxwCql1FBgs9a6tLl2hPC50lIoLz/zyBaj0VmED5Jiizt5/p133iErK4vi4mLGjx/f6O/o559/HoDnnnuOP//5zzz//PNs2bKFb7/9lilTplBYWAjAJ598woEDB1i4cCFnnXVWozUfReso19WNFk9SaoLWOqvNL6LUu8APwAPAOVrrQ0qpOGCP1vq0RTIyMzO1DC8XPrN3L/TpA7/5DZyyKGzOkSNk799Panw85/Xpg+nURbb27IFnn4W77oKXX26y+e3btzNw4EBvRS+CRFOfA6XUj1rrzGae4lPtzfOtJXle+NTChXDddfDgg871tnBuoekyYcKE5p+bkwMvvgjffANTpzZ5iuR5AR03zyulrgTuAizAK8DnOJcCOAr0AZ5sajciyfPCb3JyYPBguPXWM08PevVVyM+Hhx9ucftlyfMCWp/n3R3ZMk8p9THwpta68Ewn183xTNdau4aPnzq8/BAyvFwEiia2ff5m+XL+un49XzWYp983MZHXrr+eKQ1/wXr0cN7KGici+LUqzwsRVNatg9BQ52jEBr7dv5+Pd+6kaPFiMhISGNajB8NSUjgnI4OivXsBMFqtnOtqo5liixBBok15Xmv9CfDJKYdlroEIXK5++ZlGtoCz/79tGzgc3o1JdEruFlv+FzgM3KOUCgPerVt9vDkyvFwEj1O2fdZa88T337Pq0CFuGDyYaenp5BYX8/aWLUx78UXuGjmSl1yV77AwiI+Hw4f9FLwQHtPaPN8uJSUlzJkzh5kzZzJz5kxvvYwQTj/8AKNGNdoC9I1Nm3hv2zZ6x8RwVnw8h0tKeOfAASpsNgAGd+3KQ+eeS1JkJHTv/stuRkIEL5/meSH8prXFltpaKJTrTMLz3C22HNJa5ymlvgPuA/5V9/1CrfWqU0/WWu/FuftQU37btlCF8JJdu5zJuG6Hir+tWEHWoUPcOXIk19aNYkmLjmZscjJPfP89f/vxR9Qbb3Bl//7OoecpKVJsER1Bq/J8e0VHR8t2z8I3rFbYuNE53bPOos2beW/bNi7OyOAPY8ZgqtuJTmtN6uDBfLllC3/67DPu+PprXpk2zbld9Lp1zoV2lfLTGxGi3Xya54XwG1exxZ11tlw7Eh096r14RKfl7m5E7ymlNuOsiD+jtR6qtb4buNJ7oQnhIzt3Qr9+oBSLly3jT599xpju3blmwIBGp4WbzTxy3nmMT0nh5R9/ZL0rKffoAceOQXV1sy/hztpIouMKkp+/5HnRMW3eDDU1cPbZAJRXVzN7wQL6xMbyf0aPri+0ACilOJiTw3CTiZenTsVqt/PU2rU40tKcc/pzc5t9mSD5PRdeEiQ/f8nzonM4dMg5YsXkxrgC15IAblw4DZLfc+Elbfn5u1tsqQGu0FpfprX+FkApZQESWv2KbnANL1+0aJE3mheisV27nMUW4P1t26i02bhj5EhUE1cvTQYDfzrnHFK7dOGJ77/nZEWFc2SL1s7FuJoQGhpKYWGhJOhOSmtNYWEhoaGh/g7lTHya54XwGdf0n7FjAXjh2285UVbG78eMwXLqoucNpMfEcPeoUWzJz+cT145zzUwlkjzfuUmeFyLA5OWdtkbX93v38tiSJXy365S1nN1cEkDyfOfW1jzv7jSiPwEOgLr1VvZqra3ADa16NTfJ8HLhMxUVzoTcrx9VVitL9u7l/J496d3CsMNws5n/d+65zFm6lD9+/DFvu/at37IFRo487fyUlBTy8vLIz8/31rsQAS40NJSUU/7TD0A+zfNC+MwPPzjXXOnZk5MVFTz7zTdcetZZDO7a9YxPnd67N0v27uWPa9dyVVgYat06+NWvTjtP8ryQPC9EAMnLg4yM+rt/fOst5mVn19+/bcIEXvnVrzC6RjampJxxswvJ86Ited7dYssDwIs4dxXqDtyKc66nEMFt927nbf/+fLJ0KWVWK5f27XvGp/WJjeWagQP5x/ff87vzz2eE2ewstjTBbDaTnp7uyaiF8AbJ86JjWrfOOapFKf7y9deU1dTw+GWX1e821BKlFDcPG8bvv/2WoxkZJDczskXyvAgSkudF55CXBxMnArD96FFe3LCBMd2786dzzmHh9u28npXFiWPHmDt6tHP9xR494OefnUsCNDNyQfK8aAt3pxH9oLXOAtBafwcUeS8kIXzINZSwXz9eX7WKHlFRjGiwBXRLfj14MPEREdz3+eeQnNxssUWIIOHTPC/TRYVPFBbCnj0wdixHjx7lhf/+lwtSU90qtLiM7NaNcb1785/CQvTGjc71X4QITpLnRcdXXg7FxfXTiB5dsgRz3TIAMaGh3DZiBNcOHMhnu3ax/MAB53NSUpxbP2/f7sfARUfkbrElTSllAqi77eW9kITwobpiy3abjdV79jCzT58m12ppSqTFwrX9+7Ns+3a219ZKsUUEO5/medd0Udn2WXjF/PnOr8cfd94/doy/3nQTNoeDm4YNa1VTSinunjSJr4qLUVarc8FdIYKT5HnR8bmmA6WksC8/n4UbNnBFv37ENBixMmf4cAbGx/PX7GyKKip+WSRX8rvwMHeLLV8DuUqpTcA+YKn3QhLCh3buhJ49mf/++5iNRi7q3btVT5/Zpw+RFgv/rahw7lRx/LiXAhXC6yTPi46nrtP9bW4ur65YwcRevUiJimp1M4kVFfwcHu6808xUIiGCgOR50fG5ii09e/LYwoWgNbP69290islg4P+OHUuZ1crTX30FiYnQwpIAQrSVW8UWrfWXwFBgNjBMa+3V8YAy7FD4xPz5sHo19rAw3pk/nytGjGhU9XZHuNnMZX368HlxsfOAJGkRpHyd54XwiUOHICaGTw4dosJm47qBA9vUjMVoJG38eA4DlStWeDZGIXxE8rzoFA4dAsCRnMxX+/YxKimJRFexvIGM2Fimpafz4vLlHCouliUBhFe4O7IFQAEngC5KqT97JxwnGXYofEJrOHGCPQYDRZWVzDnvvDY1c+WAAWxzTT2SJC2Cm8/yvBA+cegQjpQUPt6xgxHdutE/Pr7NTZ133nmsA6q/+85z8Qnhe5LnRcf2+ecArH75ZY5VVDC9hVHrtwwbhsPh4PY333Su27J5s/PvAyE8xK1ii1LqLWAV8E/gHeBGL8YkhG+Ul0NlJV8XFNAnMZFJpwwxdFd8WBgj09M5AlSvX+/ZGIXwEcnzosOx2eDYMbYaDORXVbV5VItLYmIiuQkJxJ08Se2xYx4KUgjfkTwvOoWiIoiK4p/r1xNuMnFez57NnpoUGcmsfv34et8+TsbGQkGBLAkgPMrdkS3RWushWuvJWutJOLeKEyK41SXTpQUF3HbeeW4vjNuUqwcMYDNQunq1h4ITwud8mudluqjwusOHweHgvSNHSI+OZmxycruaG5CVRUJSEgAbf/tb51RUIYKL9OdFx1dUhD06mo9+/JGJqamEmkwtnn7NgAEAfHjkiPOAjFIXHuRusWWbUiqywf1YbwQjhE/VFVsOGAwMVIqsrKw2N5URG8vB6GiiDx9GW62eilAIX/JpnpfposLr6hZJ/KSggEv79m1XQd2lV79+2IH9MopRBCfpz4uOr6iIPKC8poYZGRlnPD0xIoLJqak8s3Wr84AUW4QHuVtsuRk4oZTKVUrlAm96MSa54il8ovzgQazAgN69iQoJaXd7jiFDCAE2f/RRu9sSwg98mueF8LpDh6gyGjliNDI1Lc0jTRpCQzkeGUnM8eMcKynxSJtC+JDkedHxFRWRXVzMkORkBnft6tZTrhk4kINWK2XR0bL9s/Aod4st/9Jah2ut07XW6cC93gxKrngKX9i/bRt7gKsGDfJIe1HnngvA5gULPNKeED7m0zwvhLc5Dh5kk9ZcMWqURwrqLuH9+zMaeHftWo+1KYSPSJ4XHVtFBVRW8mN5OXNasURAv7g4Rnbrxg+VlehNm7wcpOhM3N36+X6llEEp1VUppbTWb3k7MCG8KT8/H0NBAVWxsaRERXmkzerUVGxKUbpmDQ6HwyNtCuErkudFh+Jw4Dh0iA0OBzeOHevRpo+bzcQC//nmG7TsWiGCiOR50eEdPgzAcaORG88+u1VPvaRPH9bZbOicHKiq8kZ0ohNydzeiacA+4G3gV0qp27walRBe9sK8eWRoTUis56YrO0wmjsXG0ru8nO+//95j7QrhC5LnRYeSn4/JZmO3xcKUdu5CdKqyuu2jUysqWC2Loosg4us8L8sCCF+r2rULgITERLZs2NCq545PSWG70YjB4QDX+i1CtJO704hmAgOANVrrfwHtW9JfCD8qKipi0csvEwJYEhI81u6ArCwMUVEMBz56+GGPtSuEj/g0z0snXHhTdW4uAMZu3Vi7Zo1H267s0oVak4nxBgNvvPGGR9sWwst8mudlWQDha9mffgrAMDcWxj1ViMlEaI8eANiysz0al+i83C225GmtqwHXeNkaL8UjhFfNnz+f2bNnk1xRATg7zZ5U3bUrPYCsDRtkKpEINj44r2x7AAAgAElEQVTN89IJF960d+NGbECfPn0837jBQFl8PJPNZj766COKioo8/xpCeIf050WHlv3ZZwD0qiuatFb/9HSKgSP/+Y8HoxKdmbvFln5KqfuBQUqpu4EUL8YkVzyF1zgcDr777jvG141oqfLQei0uZXFxAKSUlnL//fd7tG0hvKxVeV4plaGU+kAp9X+VUi8qpR6qOx6nlJqvlLpfKfWWUqqbL4IXoqGqffvYpRSDu3f3SvtlXbuSbrWiqqu58847mT9/vldeRwgP82l/Xghf2rhxI5HFxZSbTGAytamNkd27s8VgwL5+vYejE52Vu8WWuUAXoCuQhOxGJILUtm3bKCwsZHxiIrVmM7bQUI+2Xx4Xh1aKs4Eff/zRo20L4WWtzfNxwIda62e11vcA1ymlRgFPAv/VWj8NfA4858WYhThNcXExyWVlFERFYXBzJ4rWKu3aFYPWXJSYyKpVq2ShXBEsfNqfF8KX3njjDXophT0yss1tWIxGjkdE0D0/H22zeTA60Vm5VfbTWpcBD7ruK6UGADuaO18plQE8DmzEWTUv1Fo/qpSKA57GuThXX+BBrfXxtocvROts2LCBiIgIMux256gWD3fEHSYTFdHRTKqq4q+bN6O1dnvbOSH8qbV5Xmt96mUfA1ABzACeqDu2BnjHs5EK0bKv332Xa4H93btT66XXcC2Se0VKCp9u3Mj+/fu99EpCeE5r87wQwaKyspJ//etf/CEsjNqIiHa1VZuQQFhZGXuXLiXj0ks9FKHorNwqtriGhzcwAbighae4rnh+Uff8HKXUEuC3OK94/lspNRPnFc8bWx+2EK1ntVrZvHkzI0aMIHb7diqio73yOuVxcQw7eJDCwkI2b97M8OHDvfI6QnhSG/J8w+deDnyttd6hlEoEyuoeKgVilVImrXWjv3vz8/PJzMysvz9nzhzmzJnT5viFcNmxcCEAESkplHjpNWxhYVRHRDDa4cBisbBq1SovvZIQntOePC9EIPv8888pLS0lxWymoB0jWwBiUlNh3z72fPSRFFtEu7k7oW0EzuHgAL04QxVcrniKQLRixQqqqqoYN3gwkd9/T36vXl55nbL4eJL27SMVZ/KXYosIEq3K8y5KqUnAJJzD0wFOAFFAMc7h6kWnFloAEhIS2NDKbRmFOJOXX36ZmnXrAKiIjfXqa5V27UrygQOMHj2aDRs2UFVVRVhYmFdfU4h2alOeFyLQvfPOO4xMSSE0L4/qdhZbIpKSqAKqPLyTneic3F2zZY7W+p26r8doRXJueMUTaPKKZ6siFqKNPv30U0JCQji7bkRLpZdGtriGl89MSuLzzz8/w9lCBIxW53ml1AzgQuAeIEkpNQ5YAoyrO+XcuvtC+MTOnTsZYrdTGhJCbUiIV1+rLD6eyKIipgwaRM3/b+/Ow6uq7sX/v9dJTuaZTGRiCIHEJEBImBVQUEGgiq3aVm+f9kv1W27r0J/V3vb6uz/vt/Wq2K/UR+sApWoVFJQZFaWgBBCBMBMIY8jElJDhJCQnyclZvz/OSRhkyHCmJJ/X8+wnOSvr7P3ZZOfDOmuvvVZjI+vWrXPq8YRwgE6354XwVKWlpaxfv55f3XUXQJc7WzAYKAwOJqK4mKamJgdEKHqz9na2pCmlJti3GcCd7XnTZXc8f2svar3jCTe449k6vLx1k1n+RVe1tLSwcuVKhg4dStT58wBcDAtzyrEuhoVhNRiYHBLCvn37KCwsdMpxhHCwDuV5+2S4S4AxwNfAKmAItvkA7lRKPQfcD/zOuWELccm+ffsYoRTmyEinH6vWfowxShEYGMinn37q9GMK0UWdas8L4cmeeOIJtNZE1dru5ztipdELiYlktrTw3bZtXd6X6N3aO6rkNWAPoLCNTPnzzd5gv+N5G7Y7nn2VUv24dMezhBvc8ZTh5cLRtm7dyvnz57nvvvsIKyzEYjRi7uIEWtejvbyoi4hgRGMjAKtWreKpp566ybuEcLsO5Xmt9S7gerePHnVsaELcnNVq5djevQzWmpKICKcfry48HKvBQN+iIrKyslizZg1msxk/B69yJ4QDdbg9L4Sn2759O8nJycTb292NDmjfN2VkEH7oEDs/+YQJEyd2eX+i92rvyJZfaa3/l9b6F1rrJ64xJ8sV5I6n8DTLly/Hz8+P9PR0ws+coTo2Fgztvfw7riYqiviyMrLT01m+fLnTjiOEA3UozwvhafLy8kiqrcWArSPE2aze3lQkJhJdWEhWVha1tbV8/fXXTj+uEF3g0jxfU1PDY489xpo1a5x5GNGLHT16lNOnT5OdnU1IebltdLl312eoMCUnA1Dx1Vdd3pfo3dr7afMBpVSiUirpqu2la1XWWu/SWgdprSfZt5Fa6/e01pVa60e11n+2J3pZ9lk4ndaa5cuXc/fdd+Pn50f46dNU9e3r1GOaoqLwslj49ZgxbNmyhdOnTzv1eEI4QIfyfFdJI1w42urVqxmhFOCazhaA8gEDiCwqIjUlBR8fH+bOneuS4wrRSS7N86GhocyfP5+ZM2c6Y/dCtN3QzMrKIriiApODHiGtjI+nRSmCjh2jqqrKIfsUvVN7O1umApuBD+xfV2BbSegBJ8UlhMPk5eVRUlLC/fffj9FsJriykmoXdLYATA8NRWvNsmXLnHo8IRzApXleGuHC0VatWsXEkBDMAQEOGUbeHucHDMCnsZGoigpSU1M5cOAAWmuXHFuITpD2vOhRli1bRv/+/YmIiCCkoqKt/d1VLUYj5/v0YTjIiEXRJe3tbFkCDNBaTwQGAB9rrW8HHndGUHLHUzjSsmXL8Pb2ZubMmYSdPQtAZVycU4/Z7OdHVWwsDevXEx8fz9KlS516PCEcwKV5XghHOnnyJAcPHiTHYKAiKQnsI1yc7dyAAQDEnjhBZmYmFy5c4PDhwy45thCdIHle9BhFRUXk5eUxYsQIvJqaCKyublsR1BFMyclkAevXr3fYPkXv097OllBtv1WjtbYCcfbvP3dGUHLHUziK1pp3332XwYMH88knnxB25gyA00e2AJgDA4k7coRpERFs2bKFsrIypx9TiC5waZ4XwpHWrFmDN9DfZOJCUpLLjmuKjqYuLIy4ggIyMjIA+OwzWe1ceCzJ86LHeOaZZwAYMWIEw7/4AqU1gQ585OdCUhJxwJ4vvnDYPkXv097Olhil1N+UUk8ppd4EHDNGSwgny8/P5/z582RlZQEQUVaGxdvbYc903ogpOhpjUxP32nvZP/nkE6cfU4gukDwvuq1Vq1bxg+RkvFtabCNbXEUpytLSiC8oICIsjISEBOlsEZ5M8rzoMQ4cOEBcXBxRUVEE1NQAUB8a6pB9p+bmtnXchBcVUVhY6JD9it6nvZ0ts4F8IMX+dbbTIhLCgZYtW4ZSimHDhgEQXVhIRVIS2svL6ceusT83OqS+nuGJifIokfB0kudFt1ReXk5ubi4/SU0FcG1nC1CWmorfxYtElpSQkZHBli1bqK6udmkMQrST5HnRIzQ0NHD8+HFuueUWAAJMJrRS1IeEOOwYrROty6NEoiva1dmitW4ClgJvAe9rrRudGpUQDvLpp5+SnJxMaGgoqqWFyOJiyvv3d8mxzUFBNPr5EXL+PA9mZ7Nt2zaKi4tdcmwhOsrVeV7m5hKO8sknn9DS0sKk4GCafH3bOrpdpSwtDYD4w4fJzMykpaWFr2S5UOGBOprnlVLJSqmPlFLPKKVeU0r9l708Qik1Xyn1H0qphUqpGBeEL0SbLVu2YLFYSLPn34CaGsxBQQ69mdri40NDUBDjAwJYu3atw/Yrepd2dbYopR7BNmv5fwGTlVL/6dSohHCA/fv3c/DgQUaOHAlA+OnTGJuaOG+f0NDplMIUHU1oeTn9rFYAnn76adccW4gOcnWel7m5hKO8+uqrxMXFYdm5kwuJiWBo76Bdx2gIDaUyLo74ggIGDhxIRESEPEokPFIn8nwEtkl0X9FaPwn8WCmVDfwP8C+t9UvASuAvzoxbiKutX78eb29vUlJSAFtniyNHtbSqi4hglNHI+vXrqa+vd/j+Rc/X3hbJMK11GrBLa70KcOo0/3LHUzjCokWL8Pb2JicnB4DoU6cAXNfZgu1RIr+LF0k2GEhKSmLXrl0uO7YQHeTSPC+EIxQWFnLixAnGjBxJn5ISlz9C1KosLY3YY8cwWixMmzaNL774gpaWFrfEIsQNdCjPa6132uu1MgAXgenANnvZVvtrIVxm/fr1DBw4EF9fX1RLC/61tQ6br+VydeHhRNXU4GM2y6NEolPa29lSY/+q7V+dOuGF3PEUXWW1WlmwYAFpaWkEBQUBtvlazIGB1LpgctxWJvtw9pDycnJycigsLOSUvdNHCA/j0jwvhCN89NFHADxqMmFsasLY0EBqbq7L4yhNS8PbYiH2xAmmT59OeXk5O3fudHkcQtxEp/O8UmoW8KXWugCIBmrtPzIB4Uop76vfU25v+7Ru8+fP70LoQtiUl5ezd+/etkeIQioqMFitzulsiYgAYLSPD6tXr3b4/kXP973EeB0xSqm3gb5Kqb9wKUkL4ZFyc3Opqqrihz/8YVtZ1KlTtlEtynU37OvCw2nx9iakvJzse+5h+fLlfPLJJ23L1QnhQSTPi25Fa82iRYvIjIqif6Nt6onWhrGrtHbsGJqbsRoMDP3ySwKTkjAYDHz22WeMGTPGpfEIcROdyvNKqduB24Gn7EXngWCgGggBqrTWlqvfFxUVRV5enkMCF6LVhg0bANo6WyLKygC46KSRLQBTY2J4ac0aWlpa8HLBIhui52jvyJangN1ACXAE+L3TIhLCARYtWoSvr2/bKkTeZjPhp0+7bHLcNgYDpshIQsvLiYyMpH///rIqkfBUkudFt7J//34OHTrElP79CaqspMXLyynP7LeH1WikJjqaPmVlRAQGMm7cOFatWnXzNwrhWh3O80qp6cDdwJNArFJqLPAZMNZeZbz9tRAu8a9//YuwsDD69esHQGRREVaDgYthYQ4/VrO/PxdDQxnj50d5eTnfffedw48herb2drZsBfK01r/RWi/QWludGZQQXWE2m/nkk0/IysrCx8cHsD1CZNCa867ubAFqoqMJrKrC5+JFsrOzycvL4+TJky6PQ4ibcGmel7m5RFe1zss1KSmJ4IoK2x1IF0+Oe7kL8fEEmEzsXLuW+Ph4Dhw4wMGDB90WjxDX0KE8b58MdwkwBvgaWAUMAf4I3KmUeg64H/idc8MWwkZrzYoVKxgwYAAGe76PKiriYmioQ1ciulxFUhKD6+owGo3yKJHosPa2So5qrXe3vlBK9XFSPEJ02eeff05NTQ2jR49uK0s8dIgWLy/O2Gctd6XqmBgU0PfYMbKzswHbUqVCeBiX5nmZm0t0hdVq5aOPPmLq1KmEG40EV1a2zZHlLhcSEgDoU1pKTk4OBoOBRYsWuTUmIa7SoTyvtd6ltQ7SWk+ybyO11u9prSu11o9qrf+stf6F1vqc80MXAo4dO0ZlZWXbI0RoTWRREXV9nNdkuZCYSMS5c9x1220yYlF0WHs7W44rpaYqpfoppZJw8vByueMpuuLDDz8kNjaW1NTUtrKE/HxMkZEM2rGD1Nxcl06gWNunDy1eXsQfPkyfPn0YM2YMS5YscdnxhWgnl+Z5Ibri2WefpbS0lJiYGIKqqjBYrS6d/PxaGoOCqAsPp09pKSEhIaSlpbF48WJZlUh4EsnzoltrXRGotbMluKICv/p6ap04X1dFYiIGq5WfjRjBkSNHOHLkiNOOJXqe9na2zMGWkN8D3gd+eMPaXSR3PEVnzZs3jzVr1pCent42vDCgupo+ZWVU9e3rlpi0lxc10dHE2ZNzYmIie/bs4dixY26JR4jrcGmeF6Irtm/f3jYvV3BFBQAmN3e2gG10S0hFBb51dYwbN47i4mI+//xzd4clRCvJ86JbW7duHZGRkUTZRzJGFRUBzp0c/UJSEgBT7KNnli1b5rRjiZ7nhp0tSqlXlVL3AH/QWt/eugH/2zXhCdExu3fvxmKxXPEIUUJ+PgBVcXHuCovqmBgizpzBv6aG7OxslFJ88MEHbotHiFaS50V309jYyO7duxk2bBi+vr6EVFTQGBBAU0CAu0PjQnw8Smv67d9PVlYW8fHxvPbaa+4OS/RykudFT2A2m9m4cSMZGRko+8qiMSdPYvH2dsrkuK3i8/Np9vHBvHAhycnJvPXWW047luh5bjaypRHYAAyzJ+pBAFrrfzk9MiE6Yfv27cTGxpJk74UGSMzP52JoqFMT8c1Ux8YCEHfkCOHh4aSlpfHee+/J8HLhCSTPi25l3bp11NfXt3Wqh1RUeMSoFrDdXW0MCKD/3r14eXnx61//mg0bNpBv7/QXwk0kz4tub/PmzdTX15ORkdFWFldQwNlBg5w2OS4ASnExPJygqipycnIoLS2loKDAeccTPcrNOlvMWutGbLOMG7TWx10QkxCdUlxczLFjxxg1alRbj7dqaSH+8GFKb7kF7GXuUBceTmNAAPH25Dx+/HhKSkrYuHGj22ISws4teV7m5hKdtXjxYoKDg0lLS8O/pga/ixc9prMFpShPTCQhPx+f+np8fX0xGo3MmTPH3ZGJ3k3a86Lb++KLL/D19WXIkCEA+JtM9Ckro6x1slwnqo2IIKiqipFDh6KUkrkXRbvdrLNFA2itW4C25eGUUk59xlMa4aIzFi9eDMCoUaPayuIPH8avvp7CrCx3hWVjMFDbpw/99+4lNTeXB2prCQ8P591333VvXEK4Kc/L3FyiM0wmE6tXryY7OxsvLy+iCwtt5Z7S2QKcHzAAb4uFAXv2EBQUxKhRo/juu++orKx0d2ii93JLnhfCkb744gsmTpyIj48PADkrVwJgbGhw+rFNUVEYrFaSTSZSUlL4+OOP0Vo7/bii+7tZZ8vdSqm5Sqm5wG2Xff+kM4OSRrjoKK01H374IcnJyW2TZgEM3L2bJj8/28gWN6uOicHv4kX8amvx9fLipz/9KStWrKC6utrdoYnezS15XojOWLFiBWazua1TPebkSawGg1MnR+youogIaqKjSd6xA4A77riD5uZmFixY4ObIRC/mljwvN0+Fo5w6dYqCggKmTZvWVhZ+9izNPj7UhYc7/fitHfoxJ06Qk5NDQUEB+/fvd/pxRfd3s86WJuCifVt72ffNTo5LiA7Zv38/+fn5V0yMa7BY6L9nD6eGD8dqNLoxOpsq+7wtYefOAfCLX/wCs9nMRx995M6whOh0nldKxSql/q6U2nlZmZ9S6g2l1B+UUv9QSg12VuCi91m8eDEDBgxg4MCBgK3hWxce7tzn9TtKKY6PGkX8kSMEVFeTkJDAkCFDeOONN2huluaTcAu3tOfl5qlwlFWrVgG20Y0AymolorTUttKoob2L63Zes78/DUFBxJw8SXZ2NgaDgWeeecbpxxXdn/dNfv6s1nrn1YVKqewbvUkpFQv8GRimtR5pL/MD/gKUASnAS1rro52KWoir/O53v8PLy4vsbNulmZqbS3hZGX719TT5+pKam+vmCKEhJIRGf3/Czp7l7KBBjBgxguHDh/P222/zq1/9qm2eGSFcrFN53u5WYBUw/LKyp4BirfVcpVQmsBC4zSGRil7t9OnTrF+/nqlTp6KUwmg2E3PyJKWpqe4O7XuOjxpF9tq1JOflcWDKFCZPnsybb77JihUrePDBB90dnuh9upLnhXC7RYsWkZiYSKz9xmXssWP4NDZScdmCGM5miowk9vhxggIDyczMZPv27VgsFry9b/ZxWvRmN+wKvFZitpfvusl+Wxvgl396bG2AvwjMw9YAF6LLLBYLO3bsIDMzk6CgoLby6KIiLEajrdfbEyhFdUyMbWSL1ixYsID09HT279/Ptm3b3B2d6KW6kOfRWn8K1F5VPB3YZv/5AWyrX4R0NU4h3n//fbTWjB07FrCtQmGwWqmKi3NzZN/X98gRaiMiyNiwgdTcXH5UVUVycjJ//etf3R2a6IW6kueFcLejR4+yc+fOK0avD9y1ixYvLypdmP9NUVEEmEwEV1QwduxYTCYT//qXLOglbswp466kAS5c6auvvsJkMrU1wAG8mpqILC6mvF8/jxpeXh0bi4/ZTEBNDWCbzNfPz4+33nrLzZEJ4TDRXJn/TfayK5SXl5OTk9O2zZ8/32UBiu5Ha80//vEPUlJSiImJASAxP58mPz+Pmhz3cmcHDiS4spLgigq8DAZycnLYtm0bf/jDH9wdmhBCdBuLFi1CKUVOTg5ge4RowJ49VMbHY3XhqJIa+/89cUeOkJGRQWBgIO+//77Lji+6J+c/5HZJuxrgII1w0TH//Oc/CQwMJCMjo60s+tQpvFpaODNokBsj+75qe6IOP3MGAD8/P8aMGcPSpUupqKhwZ2hCOMp5IPiy1yH2sitERUWRl5fXtj322GMuC1B0P7m5uRw/fpzx48fbCrQmIT+fstRUj+pQv9y5gQOxGI3EFxQAMG7cOPz8/NiwYYObIxNCiO5Ba81bb73F4MGDCbdPhBtXUECAyUR5v34ujaU+JIT6kBDijhzBaDQycuRIVq5cSY39BqoQ1+LKzpZ2NcBBGuGi/aqrq1m5ciUjR4689Myk1vQ9fpy68HCPWqECoDEoiIuhoUSUlbWVTZgwgaamJlkGWvQUnwFjAexztuzTWpvcG5Lo7hYuXEhISEjbvFyh584RcuECpenpbo7s+qxGI2cGDSKquBjfixfx8/Nj/Pjx7Nq1izP2DnchhBDX991331FeXn7FI0Qp27fT6O/Phfh41wajFGWpqbYOdK0ZM2YMZrOZTz/91LVxiG7FlZ0t0gAXDrd06VIaGxuveIQoqqiIoKoq26gWD5x09kJ8PKHnz+NTXw9AfHw8gwYN4pVXXsFqtbo5OiHaTyk1Efg3oK9S6jmllD/wGtBPKfUc8DQw250xiu6vpqaGTz/9lJ/85Cf4+PgAtkeIAEpuucWdod3U6cG2xbjijtrWA5g0aRJWq1VG7AohRDu88847+Pr6tnW0ezU1MTAvj8q4OLeMatRKEWAykbV2Lf379ycmJoaXXnrJ5XGI7sMpnS3SABeuYLVaee211xg2bBj9LhtKmPX55zT7+HC+f3/3BXcDlQkJGLRu+7AAMHHiRMrLy2WiLdGtaK03aa1na63jtdZ/1lo32Ldf21//XFadE121ePFiGhoamD37UrMhIT+fqthY6jx0vpZWjUFBVCQmEnv8OEazmejoaDIyMnj77bdpampyd3hCCOGxKisrWbJkCaNHj8bPzw+A/vv24W2xcH7AALfEVG1fDSn8zBmUUowdO5bjx49z8uRJt8QjPJ+zJsiVBrhwus8++4xDhw7xzDPPtC2bHFVYSP99+yhNS6PFfgfU05j69KHJ15ek/fvbyrKysggODpaJcoUQ4jJWq5V58+aRnZ3dNjmiT309cUeOePQjRJcrTUvD2NTELd98A9hGt5w9e5bly5e7NzAhhPBg77//PmazmQkTJrSVDdq+ncaAAGqirzntp9OZg4KoDw4m4vRpAEaPHo1Sig8//NAt8QjP58rHiIRwqJdffpl+/frx0EMPtZXlrF5NQ1AQp4cMcWNkN2EwUBkfT9LBg6iWFgCMRiPjxo1j9erVlJaWujlAIZyvpqaGxx57jDVr1rg7FOHBVq9ezbFjx8jKymLBggUADNqxA2+LhWOXPcPvyWojI6ns25dhX32Ft9lMeno6UVFRPPfcc/I4kRBCXIPWmrfffpuxY8eSmJgIgF9tLYn5+Zzr39+t0wRUxscTdu4cXk1NREREMGTIEP75z3+itXZbTMJzeWRnizTCxc1s3bqVrVu3MmbMGP7xj38AkLxjB4mHDrF36lRajEY3R3hjFYmJ+NbXk3TgQFvZhAkT0Fq3faAQoicLDQ1l/vz5zJw5092hCA+lteaFF16gT58+jBgxorWQ1C1bqEhMpCIpyb0BdkDR0KH4XbxI+qZNGAwGJk2axIkTJyguLnZ3aEI4jbTnRWd9/fXXHD16lDlz5rSVDczLw2C1uu0RolZVcXEYrFbijhwBYOzYsZw4cYKNGze6NS7hmTyys0Ua4eJmXn75ZQIDA9uWAQ26cIFbFy/mbHIyB++4w83R3VxlXBwXw8JIy81tK4uMjGTq1KksWLCA5uZmN0YnhBDut3z5cvLy8pg+fTpe9okQc1auJLKkhKrYWFI3byb1shzqyWojIylOT2eofXRL6zLQ69evd3doQjiNtOdFZ7311lsEBgZiMl1aSyVl+3YuJCRQHxbmxsigOjqaFi+vthum2dnZREZG8uabb7o1LuGZPLKzRYgbyc/PZ82aNdx+++34+vqiWlqYPm8eXs3NFKenM2TrVneHeHMGAwW33krioUMEl5e3Fc+ZM4czZ86wevVqNwYnhBDuZbFYeO6550hLS2PMmDFt5bEnTtDi5eWxE6DfyO4ZM/CvqyN90yYCAgK49dZbycvLo6ioyN2hCSGExygrK2PlypWMHTsWo32kenB5OTGFhRwbNcrN0YH28qIyPp4Be/agrFaMRiOzZ89m1apVMhWA+B7pbBHdzqOPPorRaOT2228HYNSKFYSWl3N81CjMwcFujq79Dt96K1op0jZvbiu75557SEpKkolyhRC92uzZsykoKGDChAlto1q8zWaiT52iIinJYydAv5HzAwdeMbpl8uTJAMydO9fNkQkhhOeYN28eWuu2dj7YRrVopTjhAZ0tAOVJSQSYTMQeOwZASEgIVqtVpgIQ3yOdLaJbKS0tZfv27dx6660EBQWRtG8fw9av53RKituf4eyo+vBwioYN45ZNm/CvqQFg4cKFZGVlsWHDBnbt2uXmCIUQwvUaGhpYu3Yt/fv3Jysrq608c8MGvJubOT14sBuj67zU3FwuJCTgX1fHpHffZdzBg9x6660sWLBARrcIIQS25Z7feecdfvzjHxMZGQmAag38h34AABjbSURBVGkhdfNmylJTuRge7uYIbSrj42n28SE5Lw+wTQWQkZHBO++8Q0NDg5ujE57EIztbZEItcT3z5s0D4H+HhHDLxo1Mev99LoaGciI7282Rdc72WbMwWCyMW7q0rez2228nICCA559/3n2BCeFkkufF9bzwwgtUVlZy//33o+wrTvjW1THsq6+oSEig1t4A745aVyZKOHwYg8XCtGnTUEpJvhdCCODNN9+krq6O3//+921lA3ftIqi6mqqYGI+Zp8vq7U3RsGEk79yJ0WwG4K677uLcuXO8/fbbbo5OeBKP7GyRCbXEtVRUVPDOO++Qk5ND36AgEg4fxu/iRY6PHIm2DzPvbkwxMeydNo3kvDyS9u8HwN/fnzvvvJO1a9eyadMmN0cohHNInhfXcuDAAebOncuYMWMYMmRIW/mIzz/Hu7GRwuHD3RidYxQNHYpPYyPxBQVERETwxBNP8N5777Fjxw53hyaEEG5jMpl4+eWXyczMZNu2bQAoq5VhX31FfUgIVXFxbo7wSgcmT8a3oYFU+3QAgwcP5o477uDll1+mvr7ezdEJT+GRnS1CXMsrr7xCfX0906ZNw7eujsT8fM4nJVETE+Pu0DolNTeX1NxcGgIDqQsL446FC4mwT6w1ZcoU+vXrx69//WtZmUgI0Ss0NDTw05/+lIiICH70ox+1lQeXl3PLpk0cHTeOhtBQN0boGLWRkVQkJJCYn4+fyURCQgKhoaH8+7//u+R7IUSv9fLLL1NXV8eMGTPaylI3byaypISijAywj3T0FOUDBnB68GAy//UvvO2jW0aMGCGjW8QVpLNFdAvnzp3jjTfe4Kc//SlxcXEM3LMHgMIRI9wcWddpLy/yJ02iyc+Paa+/Tsi5c/j4+PD666+Tn58vw8uFEL3CU089xcGDB3nvvfcItk92brBYuGPhQlq8vdnVg0ZBFWZlYWhpIXvtWvz9/XnooYfYtWsXf/rTn9wdmhBCuFxJSQmvvvoqo0aNor99tbnAykpGrVxJ2ZAhlHvgCnSpubmc79ePwJoaZrz6KqmbNjHzzBnS0tJ48cUXqbHPxyh6N+lsEd3Cww8/TENDA0OGDKHvkSNEFRdTkp5OY2Cgu0NziMbAQArGj8fY0MD9L7xAzooVnDlzhvHjx/Piiy+ybt06d4cohBBOM3/+fObPn8/UqVMpLi5uKx+5ciUxhYVs+tnPPGZiREdoCAnhTEoKaZs3E3bmDNnZ2fz85z/nhRdeYMuWLe4OTwghXOqBBx7AYrEwa9YsALyamvjBK6/g1dRkmxTdw0a1tDJFR3Nq6FCii4pI3rULrFZmzZpFRUUFL730krvDEx5AOluExyspKSE3N5cxY8YQGxnJ+I8/xhwYSGlamrtDc6j6sDD23XUXVm9vhq1fT8p33/HQQw8RHx/Pgw8+yL59+9wdohBCONy3337Lb37zG9LT07n33nvbypN37mTY+vXkT5pEYTedBP1GijIzafb1ZcIHH2BobiYrK4uIiAjuu+8+qqur3R2eEEK4xPr169m+fTt33nknERERoDW3Ll5McGUlBePH0xAS4u4Qb6gkPZ3S1FTijxxhyLZt9EtM5JFHHuGvf/3rFTcPRO/kkZ0tskqFuNyTTz4JwIwZM7glN5eI06c5kZ2N1dvbzZE5XkNICHvuvpvaPn24/d13mbJ0Kb999FG8vLyYNGkSZWVl7g5RCIeQPC8ATp48yf33309SUhKzZ8/GYLA1S9K//po7/v53aqKiqIyNbZvjqiex+Pmx+eGHiT1xgvEff4yfry+zZ8+mqqqKhx56CIvF4u4QhegSyfPiZmpra3n00UeJiYlh+vTpAAzZupUh27ZRlJFBZUKCmyNsB6U4mZ1N4bBhxJw6xbglS0hPT8disfDQQw+5OzrhZh75abV1lQohli5dyooVK5g1axbxfn5kr15NaVoaF7pD8u2kZn9/9k+eTP99+0jdsoX+e/dyyy23MGv/fqZPn87mzZvb5jMQoruSPC/Onj3L6NGjaWhoYM6cOQQGBuLd2Mio5cvJ+OYbKhISKBg/vtuuNtceJ0eOZE9pKVnr1tlW2pg8mYcffpgPPviA3/72t7z++uvuDlGITpM8L27m2Wefpbi4mGeeeQaj0UjOypUM//JLqmJjKcrMdHd4HVKSkYGxsZGMb77hzODBTJkyhXXr1rF7925G9IA5JkXneOTIFiEA8vPzmT17NmPHjuXOKVMYt2QJPmYz3z74oMc+u+kwBgOnsrLYd+edAEzdsYNvExJoOXCABx54QFasEEJ0a1VVVdx9993U1tby+OOP07dvXxIOHuSB//5vMr75hgN33MGh227rkSMYr7bz3ns5NWwY45YuZezSpdw2dixPP/00b7zxBq+99pq7wxNCCKdYsmQJb7/9NlOmTCE5ORmfixdJ27yZZj8/CsaPB0P3+5hamJXFuQEDmPDBBzwwciRBQUE88cQTWK1Wd4cm3KT7XcWiVygoKGDKlCl4eXlx7733MjQ3l5QdO9g1YwbVcXHuDs9lTNHR5M2YwamhQ8koLmaf1covvvySX+TkoLV2d3hCCNFhRUVFpKenc+jQIebMmUNaZCT3vfgi97z+Ol7Nzey9806q+vbtlg3tjkrNzSV1yxZKbrmF0iFDyNywgenz5pEVHs7w4cN56qmn+Nvf/ubuMIUQwqGOHDnCL3/5S5KTk5k1axaqpYXb33sP3/p6Dt12G81+fu4OsVO0wcDGX/4SpTXTP/yQB+67j61bt7JgwQJ3hybcpOe3ZES389VXXzF27Fi01vz2qacYd+AA45YupSIhgYagoB733P7NaC8vijMzWfzii+ybNo2Z3t58uH8/h4YMQe/a5e7whBCi3Xbv3s2YMWOoqanhySef5C6LhQeef57I4mKKMjPZdc89mKKj3R2my2mDgZM5ORSMG0dkSQkP/OlPvJqayvChQ/nNb34jI1yEED1GeXk5M2fOxNfXl0cffRR/s5k7Fi6k3/79nMjOpjYy0t0hdkn8oUMcz8khprCQZ0+eJC01lccff5yDBw+6OzThBsoT747n5OTovLw8d4chXKyxsZE/Pf00n/3tbwzp04fZo0eTfvw4cUePUhUby6EJE2gxGt0dptt5mc2UbNzI1KoqwgDrmDEYfvhD+N3v3B2a6CCl1C6tdY6743CHlJQUffvttzNz5kxmzpzp7nCEk2mtWbBgAU8++STR0dE89sgjzLCvNlSRkMCpYcOoDwtzd5geoTQ9nYnvv0/8kSOcHTCA3xqNfHz0KBMnTmTdunX4ddM7vr1Vb87z0p4XV6urq+Pfxo7ljvx8fhARQR+rFb/aWrwtFrb96Ec0+/q6O0SH6bd/P/0OHGDX+PFM3r+fkLAwNm3axIABA9wdmnCwG+V5j+xskUZ4L2K1or/+mlOPP47/kSPEXvVMoykykv1TpmA1GHr+PC0dYNWaz0tKCN+8md8BBl9fvP7yF5gzB3rwZJI9jTTCpRHeo2kNjY1UVFTwm6efZuXSpdyRnMzTGRmM/fprAkwm8idO5LsHHiBl2zZ3R+tZtCbm5EkG7N2L0Wzmu8hI/lxRQUVWFu8vXkxqaqq7IxTtJHle8rywKd+4kT0PPsjkCxfQStEQHk6Tvz+nU1M5OnYsFxITe9boda0ZuHs3CQUFHBoyhHvOnKHOaORvf/sbDz74IEo+1/QY3a6zRZJzD9fUBPn5NH/6KQ1//zsh589TC2zy9iYoKYmo+HhOjB5NbWQkF8PDAXpW8nWgz0+cYH1eHi9bLEwBGtLS8F+4EMaOdXdooh2kES55vsc4fx7WrYP166GgAE6cQFdXo67TxjidkkLevfdyNiUFkBx/PV5NTSQcPkxUcTEBJhP1QB5gHjqUkY89RvikSTBkCPSCiYS7K8nzkud7tbIyGlavpvy110g6coRa4NvhwwkZOJBmf38ACiZMaKve4/4v0Jr4ggIG7N3LxcBA5nl58UF1NQwcyEdLlpCT0ytTQ4/jUZ0tSqkpwP3AeUBrrf/76jrdOTnPnz+fxx57zN1huMQ1z9VigYMH4bvvYMcOKCmB8nJobISmJrTZDGfPoqxWrMAmYIW/PyGZmdyRnIy3h06IuPrYMX5g/1DgaWoaG1l88CAcPcpfrFbigTPp6YQ9/jj+kydDcnKHRgX1+mvYhXpqI7yn53lw/7XjKjc8z6Ym+OILmD/f9lVrdHAw1X36cFxrvjt/njMNDcQFBXFb376E+vtjDgykPjSUuogIjxut6Ml5XlmtRJSV4Xf6NNbSUpLMZloH27cYjVgzMjDedRdMngy33gr2DzHX01uuX3D/ufbUPA83z/WS57uHTp9nSwts3w6ffQZbtoDJZMs9YWFYWlqo37+fkLNnASgEvo6MJHDECPpGRTn2BDrAXXn+QlISo5ctI+7oUQAagWKgLiaGhHvuIWrKFBg2DAYPBgdNmdBbrl9w/7neKM+79JOtUioAeBv4rdb6eWCoUmry1fVqamo6tN81a9Z4TP358+d7TCwdqq81X3z0ke2O5K5dts6SAweguBhqauAaS5bNnTvXllg3bIDnn7c18sLCICsL5sxBf/IJjUeOcM5k4nBtLV+Wl/NxeTl/tlr5mVI8kJDAgSlT+OGsWdyVkvK9jpatpaXtPs+O1O1M/bXHjztt/12NPdTXlznZ2fxk1ixezMjgRaMRc34+/r/6FaSkUGc0ci4+nvM/+AE1jz9O06uvwpIl8NVXtg6xo0dtd6VNJjh/nlVvvAHHjkFVle0xgJvoyDXpMde73dy5cz0mlp6ivXkeuve105Fc72mxd6R+299ITY3t/4SVK+GVV7Dedx/WiAi47z7qtm5ldVwcD0ZGYqytJeLUKUYXF7M8MZHACRPI/MEPqBk1iuLMTM4PHEhdnz5tHS3OzN2elOc7Wv/qutpg4EJiImWjR3Pmhz9kxYwZPDNgAHOMRuY1N/Pdnj00v/wy3HUXzcHBFKWkcOiRRzj58cc0VFd/b/8fvvWW7f/7DRtg0SJYuBDeeQcWL7aNTtqzB0pLwWwGuvffquR552hvru/O106Py/Na2zpIrtL2N6I1VFbC3r22NuLnn8PXX9vaigcPwsmTcPgwu59+Gh5+GKKjYfx4eOklKCmhqamJc8XFHN+0if3r17Px7Fn+X29vfh4VxZd33cWgu+9uV0dLT8zzfYqLOT5yJDtnzODo6NGUpqZyITQUw7lzhL77ru3fMyODloAAGgcNwjp5Mvzbv8Gzz8K8efDxx3z74ou29nldXbtimf/OOx2K3ZnXpCfl+Y7uv6t53tXjTscCRVrrRvvrrcB0YMPllaqv0TC4kTVr1nRobhdn1+8Ih8Sydi0cOmQbVWKxQHMz1NaCyUTcN9/A66/b/jjNZtvPvL0hJgZ8fW1/sBcuQEUF026yBrzZxwezry9NRiPeFgt5JhOEhgJgBU75+3PQz4/dgYFsaGpiW00NLZclhABvb0bHxTE+IYGH4uMJ9vG54fG+LStjfEJCu/5dOlK3M/U7yh2xh/n58eCwYViHDuWL8nJOHTuG8fx5kurrGX76NImnT9MHuNmMLp+BrWcdaPTyosbfn1pfXxpiY6kPCqLR3x+UwoCtt7bu8GEap0zBt7HRdt21biaT7WtDAwQHQ2goGVVVMHy47bpp3cLC4DoTPjZ++qntA0E7Xbd+S4vt2m/dmpqgsZH/U1oKP/6xbeSV0Wj7m7jOVrpxI3hI3vBw7crz0LF/ozVr1jDTbIbCwku5zmKx/S4bGuDiRaivt20GA7H79sHSpeDjc2kzGr//GsBqpXnZMltngta2zuXrfbVaobGRPxYV2RpC9fW26+ta+7eXqa++gn37bK+Vsu3rBsdRq1fbGreNjZfOqb7edq7e3t/bgrdssX1A9vbmWGEhF0wmrErRYu/QMDY2YmxsxMdsxqepib6lpZwJCsKrpQW0xmow0GQ02jZvb5q9vPBqacFosbCqqIgGX1/8m5qu+H2UAF8Aa4F1NTVEA/0DAxkYHs7PMjMZGhVFWDsmcnVm7vakPN/R+jerGxcaSty4cbRYrRyqqOBXe/YwOCiImAsXuKWhgYnHjzPs+HFYtAgrcNZgoN7LiwClCG5pIbelBdLS2hVLs7c3Y5SiPDwcs78/zb6+WHx86JOYSJ+kJNsS3S0tbVufrVttHfpaX8qhfn5XfvXyavs7aF6+/NLf3o02AK0xrF1ruyHU0mL7u6+r+/5mMEBAAAQE8FpJie2DTGDgpS0gwBaDwQCjRsHEiYBntQ+7gXbl+g7n+d7cnr8WiwVeew3Lxx/Dzp2X/l+orLRtBQW2a95sto0q8fKCwECGVVdDSoqto/zMGdvPGxvbcr7Fy4sWwOLlxXqzmaqICILq6jA2N980pBGAydubXSEhbI2PJ9fLi5KGBo6cOoXWmnA/PyYOGsQd/foxOTqa/7tjB7d0YDRLT87zDaGhNNg/OwH8f99+y+CQEM6UlBBQVUWmxULyiRP0PXGCeKWI1ZrWT0vjAP74RwDMRiN1wcHUBQdjDgjAy2rFp6kJ38ZG/Boa8GtoYIfZfKldEhpqy3v+/rbrpbWNMnhwW7n/tm3w7beX6j32GISEXPdcPOlvuzMDNZwV+9Vc+hiRUuonwENa6/vsr38JTNJaP3JVvQZsn99blQMVN9h1KNCRf2Vn1o/kxrG6MpaO1u/ovpOwjYJzRiwdre/sf0dn/l496RqA3nWuHbmGnRFLP621+8bTOkEH8nwtYARab7N1pzwPHfs78bTYO1Lfk/J8R+t7Uu7raH1Pun47un9PugZA8rxTtCfXS57vNvUlzztu/3Kurt83OPca7lKed/XIlvNA8GWvQ+xlV9Ba3/hhYyGEEJ6qvXk++OoyIYQQ3cZNc73keSFEb+fq2Ui3Af2UUq3zuo3H/tSCEEKIHkHyvBBC9HyS64UQ4ibcsRrRncCPsA0lbL7WKhVCCCG6L8nzQgjR80muF0KIG3N5Z0tPoJSKAF4CTgIpwB+11ueuUW8Q8BfAorX+UUff7wk6cK6PAFnYnss9obV+x17+NpB6WdXHtdYHnB54O91s2UKllB+232EZtvN/SWt91P6za56zp+riuZ4CTtmrlmmtH3ZR2J3SnqWHlVIPAf8DPKm1XtuR94qeT/J8z8nz0HtyveT579WRPC+uS/K85PnumOeh9+T6HpHntdaydXDDttTdg/bvZwIfXKfew8BjwKedeb8nbO2JFUgA9nKp824nkGL//nl3n8MNzi0AOA742l8vAyZfVec/gGft32cCm292zp64deVcPf332MlzHQDcDnwDzOjIe2XrHZvk+e/V6ZZ53h5fr8j1kuclz8vW4etI8vyVdSTPa8/O81091+7wu+zgeXp8nnf1nC09xXRsz6rCpaXuvkdrvQhousaP2vV+D9GeWO8Gdmn71WyvP83+fbBS6j+VUr9XSv1GKeXqSZlv5HrLFl6u7fy1rQd/mFIqhBufsyfqyrkC3KaUelYp9Sel1DiXRNx5Nz1XrXWh1vrrzrxX9BqS56/UXfM89J5cL3n+MpLnRTtInr+S5HkbT87z0HtyfY/I8572h+IxlFJfAjHX+NF/AdFArf21CQhXSnlrrS3t3H1X3+9QDjjXy+u01ou2f78I2K+1tiil5gJ/AP7kyPi74EZx36xOe97rSbpyribgD1rrHUqpAGC3UmqG1vq4MwPugq78brrb71V0geR5oOfneeg9uV7yvPPfK7oZyfOA5Pmb1eluOaG35Poekeels+U6tNZ3X+9nSqnW5e6qsS11V9XBxNrV9zuUA871PDDostch2IZuobXefVn5RuD3eE5ybs8Stderc91z9lBdOVe01jvsX+uVUnuxrTrgqefbrqWHnfBe0c1Inrfp4Xkeek+ulzzv/PeKbkbyvI3k+RvW6U55HnpPru8ReV4eI+qcz7ANT4LLlrpTShmUUkmdfb+Has+5fglkK6WU/fVY4At7vVcu21cKcMLpEbffNZctVEpFXDbUru38lVKZwD6ttYkbnLOH6vS5KqUmK6WmXravQXjW7/Fq7TnXDr3XSXEKzyZ5vmfkeeg9uV7yvOR50TGS5yXPd7c8D70n1/eIPC+rEXWCss3o/TJQBCQD/6G1PqeUGo5twqlMe717gZ8BQ4B/aq3n3uj9rj+Tm+vAuT4C5GCbxfuovjR7+bvAOaAe27/D/+NJ56qusWyhfXhkpdb6JaWUP7bZvM9gS0j/o6+cufx75+ypOnuu9iT9PLALiANOa63/xy0n0U7tOFcF/CcwG9gCfKi1/vJ673XLSQi3kjzfc/I89J5cL3le8rxoP8nzkue7Y56H3pPre0Kel84WIYQQQgghhBBCCAeSx4iEEEIIIYQQQgghHEg6W4QQQgghhBBCCCEcSDpbhBBCCCGEEEIIIRxIOluEEEIIIYQQQgghHEg6W4QQQgghhBBCCCEcSDpbhBBCCCGEEEIIIRxIOluEEEIIIYQQQgghHEg6W4QQQgghhBBCCCEc6P8Hh37Fu09HvWMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1382.4x345.6 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# third order statistics overview\n",
    "summary_third_order = third_order(returns, rhos, log=True, render=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Forth Order\n",
    "\n",
    "Test percentage deviations of forth order moments. Perform Kurtosis test to check deviations from normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def forth_order(df_1, df_2, log=False, render=False):\n",
    "    \"\"\"Forth order moments comparison test.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    df_1: pandas.DataFrame\n",
    "        Family 1 data.\n",
    "    df_2: pandas.DataFrame\n",
    "        Family 2 data.\n",
    "    log: bool, optional\n",
    "        Flag for printing summary.\n",
    "    render: bool, optional\n",
    "        Flag for printing distributions.\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    summary: dict\n",
    "        Forth order test summary.\n",
    "    \"\"\"\n",
    "    # type check\n",
    "    assert isinstance(df_1, pd.DataFrame)\n",
    "    assert isinstance(df_2, pd.DataFrame)\n",
    "    # interface check\n",
    "    assert (df_1.columns == df_2.columns).all()\n",
    "    # column-wise kurtosis values\n",
    "    kurt_1 = df_1.kurt(axis=0)\n",
    "    kurt_2 = df_2.kurt(axis=0)\n",
    "    # relative deviation of kurtosis values\n",
    "    dkurt = np.abs((kurt_1+eps) / (kurt_2+eps) - 1)\n",
    "    # summary table for each ticker\n",
    "    summary = {}\n",
    "    # iterate over tickers\n",
    "    for ticker in df_1:\n",
    "        # Kurtosis test for family 1\n",
    "        kurtosistest_s_1, kurtosistest_p_1 = scipy.stats.kurtosistest(df_1[ticker])\n",
    "        # Kurtosis test for family 2\n",
    "        kurtosistest_s_2, kurtosistest_p_2 = scipy.stats.kurtosistest(df_2[ticker])\n",
    "        # summary table columns\n",
    "        _name1 = 'series 1'\n",
    "        _name2 = 'series 2'\n",
    "        _name3 = 'deviations'\n",
    "        summary[ticker] = pd.DataFrame({_name1: [kurt_1[ticker], kurtosistest_s_1, kurtosistest_p_1],\n",
    "                                        _name2: [kurt_2[ticker], kurtosistest_s_2, kurtosistest_p_2],\n",
    "                                        _name3: [dkurt[ticker], None, None]},\n",
    "                                       columns=[_name1, _name2, _name3],\n",
    "                                       index=['skewness', 'kurtosistest statistic', 'kurtosistest p-value (>0.05)'])\n",
    "    # print summary\n",
    "    if log:\n",
    "        for ticker, summ in summary.items():\n",
    "            print(ticker)\n",
    "            print(summ)\n",
    "            print('\\n')\n",
    "    # plot distributions\n",
    "    if render:\n",
    "        # random selection of assets to render\n",
    "        I = np.sort(np.random.choice(df_1.shape[1], min(3, df_1.shape[1]), replace=False))\n",
    "        # initialize figure & axes\n",
    "        fig, axes = plt.subplots(ncols=len(I), nrows=2, sharex=True, figsize=(6.4 * len(I), 4.8))\n",
    "        for i, m in enumerate(I):\n",
    "            # normal distribution with family 1's statistics\n",
    "            mu_1 = df_1.iloc[:, m].mean()\n",
    "            std_1 = df_1.iloc[:, m].std()\n",
    "            norm_1 = np.random.normal(mu_1, std_1, 10*df_1.iloc[:, m].count())\n",
    "            sns.distplot(norm_1, label='$\\mathcal{N}(%.4f, %.4f)$' %\n",
    "                         (mu_1, std_1), color='k', norm_hist=True, ax=axes[0, i])\n",
    "            # distribution plot of family 1\n",
    "            sns.distplot(df_1.iloc[:, m], label='%s' %\n",
    "                         df_1.columns[m], color='g', norm_hist=True, ax=axes[0, i])\n",
    "            # normal distribution with family 2's statistics\n",
    "            mu_2 = df_2.iloc[:, m].mean()\n",
    "            std_2 = df_2.iloc[:, m].std()\n",
    "            norm_2 = np.random.normal(mu_2, std_2, 10*df_2.iloc[:, m].count())\n",
    "            sns.distplot(norm_2, label='$\\mathcal{N}(%.4f, %.4f)$' %\n",
    "                         (mu_2, std_2), color='k', norm_hist=True, ax=axes[1, i])\n",
    "            # distribution plot of family 2\n",
    "            sns.distplot(df_2.iloc[:, m], label='%s' %\n",
    "                         df_1.columns[m], color='r', norm_hist=True, ax=axes[1, i])\n",
    "            # y-axis limits\n",
    "            ymin_1, ymax_1 = axes[0, i].get_ylim()\n",
    "            ymin_2, ymax_2 = axes[1, i].get_ylim()\n",
    "            ymin = min(ymin_1, ymin_2)\n",
    "            ymax = max(ymax_1, ymax_2)\n",
    "            # setting for family 1\n",
    "            axes[0, i].set(ylabel='Frequency', xlabel='', ylim=[ymin, ymax])\n",
    "            axes[0, i].legend()\n",
    "            # settings for family 2\n",
    "            axes[1, i].set(ylabel='Frequency', xlabel='', ylim=[ymin, ymax])\n",
    "            axes[1, i].legend()\n",
    "        # present figure\n",
    "        fig.show()\n",
    "    return summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL\n",
      "                                  series 1      series 2  deviations\n",
      "skewness                      4.534668e+00  4.955355e+00    0.084895\n",
      "kurtosistest statistic        1.443584e+01  1.495241e+01         NaN\n",
      "kurtosistest p-value (>0.05)  3.078681e-47  1.502019e-50         NaN\n",
      "\n",
      "\n",
      "GE\n",
      "                                  series 1      series 2  deviations\n",
      "skewness                      4.748366e+00  4.425491e+00    0.072958\n",
      "kurtosistest statistic        1.470419e+01  1.429368e+01         NaN\n",
      "kurtosistest p-value (>0.05)  6.059560e-49  2.396157e-46         NaN\n",
      "\n",
      "\n",
      "JPM\n",
      "                                  series 1      series 2  deviations\n",
      "skewness                      3.549054e+00  3.648364e+00     0.02722\n",
      "kurtosistest statistic        1.300538e+01  1.316627e+01         NaN\n",
      "kurtosistest p-value (>0.05)  1.140379e-38  1.372020e-39         NaN\n",
      "\n",
      "\n",
      "MSFT\n",
      "                                  series 1      series 2  deviations\n",
      "skewness                      7.531867e+00  7.806399e+00    0.035168\n",
      "kurtosistest statistic        1.734690e+01  1.754672e+01         NaN\n",
      "kurtosistest p-value (>0.05)  2.081629e-67  6.301047e-69         NaN\n",
      "\n",
      "\n",
      "GS\n",
      "                                  series 1      series 2  deviations\n",
      "skewness                      4.554334e+00  5.227280e+00    0.128737\n",
      "kurtosistest statistic        1.446108e+01  1.526248e+01         NaN\n",
      "kurtosistest p-value (>0.05)  2.134379e-47  1.359766e-52         NaN\n",
      "\n",
      "\n",
      "MMM\n",
      "                                  series 1      series 2  deviations\n",
      "skewness                      4.429637e+00  4.573958e+00    0.031553\n",
      "kurtosistest statistic        1.429914e+01  1.448614e+01         NaN\n",
      "kurtosistest p-value (>0.05)  2.215255e-46  1.482349e-47         NaN\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "/Users/omega/Developer/qtrader/.env/lib/python3.6/site-packages/matplotlib/figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1382.4x345.6 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# forth order statistics overview\n",
    "summary_forth_order = forth_order(returns, rhos, log=True, render=True)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "qtrader",
   "language": "python",
   "name": "qtrader"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
